Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations123849
Missing cells1269564
Missing cells (%)33.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.3 MiB
Average record size in memory248.0 B

Variable types

Numeric14
Text8
Categorical9

Alerts

remote_allowed has constant value "1.0"Constant
sponsored has constant value "0"Constant
compensation_type has constant value "BASE_SALARY"Constant
applies is highly overall correlated with viewsHigh correlation
closed_time is highly overall correlated with expiry and 3 other fieldsHigh correlation
currency is highly overall correlated with med_salaryHigh correlation
expiry is highly overall correlated with closed_time and 3 other fieldsHigh correlation
formatted_experience_level is highly overall correlated with max_salary and 1 other fieldsHigh correlation
formatted_work_type is highly overall correlated with work_typeHigh correlation
job_id is highly overall correlated with closed_time and 3 other fieldsHigh correlation
listed_time is highly overall correlated with closed_time and 3 other fieldsHigh correlation
max_salary is highly overall correlated with formatted_experience_level and 2 other fieldsHigh correlation
med_salary is highly overall correlated with currency and 1 other fieldsHigh correlation
min_salary is highly overall correlated with formatted_experience_level and 2 other fieldsHigh correlation
normalized_salary is highly overall correlated with max_salary and 2 other fieldsHigh correlation
original_listed_time is highly overall correlated with closed_time and 3 other fieldsHigh correlation
views is highly overall correlated with appliesHigh correlation
work_type is highly overall correlated with formatted_work_typeHigh correlation
pay_period is highly imbalanced (51.9%)Imbalance
formatted_work_type is highly imbalanced (62.2%)Imbalance
work_type is highly imbalanced (62.2%)Imbalance
currency is highly imbalanced (99.8%)Imbalance
company_name has 1719 (1.4%) missing valuesMissing
max_salary has 94056 (75.9%) missing valuesMissing
pay_period has 87776 (70.9%) missing valuesMissing
company_id has 1717 (1.4%) missing valuesMissing
views has 1689 (1.4%) missing valuesMissing
med_salary has 117569 (94.9%) missing valuesMissing
min_salary has 94056 (75.9%) missing valuesMissing
applies has 100529 (81.2%) missing valuesMissing
remote_allowed has 108603 (87.7%) missing valuesMissing
application_url has 36665 (29.6%) missing valuesMissing
closed_time has 122776 (99.1%) missing valuesMissing
formatted_experience_level has 29409 (23.7%) missing valuesMissing
skills_desc has 121410 (98.0%) missing valuesMissing
posting_domain has 39968 (32.3%) missing valuesMissing
currency has 87776 (70.9%) missing valuesMissing
compensation_type has 87776 (70.9%) missing valuesMissing
normalized_salary has 87776 (70.9%) missing valuesMissing
zip_code has 20872 (16.9%) missing valuesMissing
fips has 27415 (22.1%) missing valuesMissing
job_id is highly skewed (γ1 = -38.62019765)Skewed
max_salary is highly skewed (γ1 = 167.9307109)Skewed
views is highly skewed (γ1 = 53.72544033)Skewed
min_salary is highly skewed (γ1 = 168.5856772)Skewed
normalized_salary is highly skewed (γ1 = 65.29896859)Skewed
job_id has unique valuesUnique
job_posting_url has unique valuesUnique

Reproduction

Analysis started2024-10-17 20:07:49.980640
Analysis finished2024-10-17 20:09:00.726389
Duration1 minute and 10.75 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

job_id
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct123849
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8964021 × 109
Minimum921716
Maximum3.9062672 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:00.798482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum921716
5-th percentile3.8851101 × 109
Q13.8945866 × 109
median3.9019984 × 109
Q33.9047071 × 109
95-th percentile3.9060719 × 109
Maximum3.9062672 × 109
Range3.9053455 × 109
Interquartile range (IQR)10120482

Descriptive statistics

Standard deviation84043545
Coefficient of variation (CV)0.021569525
Kurtosis1606.3511
Mean3.8964021 × 109
Median Absolute Deviation (MAD)3288988
Skewness-38.620198
Sum4.8256551 × 1014
Variance7.0633175 × 1015
MonotonicityNot monotonic
2024-10-17T22:09:00.856440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
921716 1
 
< 0.1%
3903893002 1
 
< 0.1%
3903891958 1
 
< 0.1%
3903891956 1
 
< 0.1%
3903891949 1
 
< 0.1%
3903891926 1
 
< 0.1%
3903891876 1
 
< 0.1%
3903891836 1
 
< 0.1%
3903891835 1
 
< 0.1%
3903891819 1
 
< 0.1%
Other values (123839) 123839
> 99.9%
ValueCountFrequency (%)
921716 1
< 0.1%
1218575 1
< 0.1%
1829192 1
< 0.1%
2264355 1
< 0.1%
9615617 1
< 0.1%
10998357 1
< 0.1%
11009123 1
< 0.1%
23221523 1
< 0.1%
35982263 1
< 0.1%
56482768 1
< 0.1%
ValueCountFrequency (%)
3906267224 1
< 0.1%
3906267195 1
< 0.1%
3906267131 1
< 0.1%
3906267126 1
< 0.1%
3906267117 1
< 0.1%
3906266272 1
< 0.1%
3906266248 1
< 0.1%
3906266217 1
< 0.1%
3906266212 1
< 0.1%
3906266165 1
< 0.1%

company_name
Text

MISSING 

Distinct24428
Distinct (%)20.0%
Missing1719
Missing (%)1.4%
Memory size967.7 KiB
2024-10-17T22:09:01.005186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length100
Median length79
Mean length16.969483
Min length2

Characters and Unicode

Total characters2072483
Distinct characters188
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14217 ?
Unique (%)11.6%

Sample

1st rowCorcoran Sawyer Smith
2nd rowThe National Exemplar
3rd rowAbrams Fensterman, LLP
4th rowDowntown Raleigh Alliance
5th rowRaw Cereal
ValueCountFrequency (%)
inc 9637
 
3.3%
health 7593
 
2.6%
5351
 
1.8%
group 4671
 
1.6%
of 4195
 
1.4%
services 3842
 
1.3%
the 3652
 
1.2%
healthcare 3648
 
1.2%
llc 2983
 
1.0%
solutions 2659
 
0.9%
Other values (20477) 246517
83.6%
2024-10-17T22:09:01.219942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 182589
 
8.8%
173456
 
8.4%
a 137882
 
6.7%
n 128298
 
6.2%
i 125859
 
6.1%
o 124001
 
6.0%
t 118733
 
5.7%
r 116838
 
5.6%
l 86638
 
4.2%
s 85989
 
4.1%
Other values (178) 792200
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2072483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 182589
 
8.8%
173456
 
8.4%
a 137882
 
6.7%
n 128298
 
6.2%
i 125859
 
6.1%
o 124001
 
6.0%
t 118733
 
5.7%
r 116838
 
5.6%
l 86638
 
4.2%
s 85989
 
4.1%
Other values (178) 792200
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2072483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 182589
 
8.8%
173456
 
8.4%
a 137882
 
6.7%
n 128298
 
6.2%
i 125859
 
6.1%
o 124001
 
6.0%
t 118733
 
5.7%
r 116838
 
5.6%
l 86638
 
4.2%
s 85989
 
4.1%
Other values (178) 792200
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2072483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 182589
 
8.8%
173456
 
8.4%
a 137882
 
6.7%
n 128298
 
6.2%
i 125859
 
6.1%
o 124001
 
6.0%
t 118733
 
5.7%
r 116838
 
5.6%
l 86638
 
4.2%
s 85989
 
4.1%
Other values (178) 792200
38.2%

title
Text

Distinct72521
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:01.373428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length200
Median length140
Mean length31.526399
Min length2

Characters and Unicode

Total characters3904513
Distinct characters204
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60922 ?
Unique (%)49.2%

Sample

1st rowMarketing Coordinator
2nd rowMental Health Therapist/Counselor
3rd rowAssitant Restaurant Manager
4th rowSenior Elder Law / Trusts and Estates Associate Attorney
5th row Service Technician
ValueCountFrequency (%)
38446
 
7.3%
manager 18064
 
3.4%
engineer 9867
 
1.9%
sales 8198
 
1.6%
senior 8014
 
1.5%
assistant 6791
 
1.3%
associate 6671
 
1.3%
specialist 6602
 
1.3%
technician 5261
 
1.0%
nurse 5072
 
1.0%
Other values (25149) 413120
78.5%
2024-10-17T22:09:01.595145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
404829
 
10.4%
e 377597
 
9.7%
a 258169
 
6.6%
i 257724
 
6.6%
n 247086
 
6.3%
r 246305
 
6.3%
t 231809
 
5.9%
o 175920
 
4.5%
s 167350
 
4.3%
c 131000
 
3.4%
Other values (194) 1406724
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3904513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
404829
 
10.4%
e 377597
 
9.7%
a 258169
 
6.6%
i 257724
 
6.6%
n 247086
 
6.3%
r 246305
 
6.3%
t 231809
 
5.9%
o 175920
 
4.5%
s 167350
 
4.3%
c 131000
 
3.4%
Other values (194) 1406724
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3904513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
404829
 
10.4%
e 377597
 
9.7%
a 258169
 
6.6%
i 257724
 
6.6%
n 247086
 
6.3%
r 246305
 
6.3%
t 231809
 
5.9%
o 175920
 
4.5%
s 167350
 
4.3%
c 131000
 
3.4%
Other values (194) 1406724
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3904513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
404829
 
10.4%
e 377597
 
9.7%
a 258169
 
6.6%
i 257724
 
6.6%
n 247086
 
6.3%
r 246305
 
6.3%
t 231809
 
5.9%
o 175920
 
4.5%
s 167350
 
4.3%
c 131000
 
3.4%
Other values (194) 1406724
36.0%
Distinct107827
Distinct (%)87.1%
Missing7
Missing (%)< 0.1%
Memory size967.7 KiB
2024-10-17T22:09:02.222588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length23201
Median length8184
Mean length3766.4642
Min length2

Characters and Unicode

Total characters466446460
Distinct characters1857
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101727 ?
Unique (%)82.1%

Sample

1st rowJob descriptionA leading real estate firm in New Jersey is seeking an administrative Marketing Coordinator with some experience in graphic design. You will be working closely with our fun, kind, ambitious members of the sales team and our dynamic executive team on a daily basis. This is an opportunity to be part of a fast-growing, highly respected real estate brokerage with a reputation for exceptional marketing and extraordinary culture of cooperation and inclusion.Who you are:You must be a well-organized, creative, proactive, positive, and most importantly, kind-hearted person. Please, be responsible, respectful, and cool-under-pressure. Please, be proficient in Adobe Creative Cloud (Indesign, Illustrator, Photoshop) and Microsoft Office Suite. Above all, have fantastic taste and be a good-hearted, fun-loving person who loves working with people and is eager to learn.Role:Our office is a fast-paced environment. You’ll work directly with a Marketing team and communicate daily with other core staff and our large team of agents. This description is a brief overview, but your skills and interests will be considered in what you work on and as the role evolves over time.Agent Assistance- Receive & Organize Marketing Requests from Agents- Track Tasks & Communicate with Marketing team & Agents on Status- Prepare print materials and signs for open houses- Submit Orders to Printers & Communicate & Track DeadlinesGraphic Design & Branding- Managing brand strategy and messaging through website, social media, videos, online advertising, print placement and events- Receive, organize, and prioritize marketing requests from agents- Fulfill agent design requests including postcards, signs, email marketing and property brochures using pre-existing templates and creating custom designs- Maintain brand assets and generic filesEvents & Community- Plan and execute events and promotions- Manage Contacts & Vendors for Event Planning & SponsorshipsOur company is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.Job Type: Full-time Pay: $18-20/hour Expected hours: 35 – 45 per week Benefits:Paid time offSchedule:8 hour shiftMonday to FridayExperience:Marketing: 1 year (Preferred)Graphic design: 2 years (Preferred)Work Location: In person
2nd rowAt Aspen Therapy and Wellness , we are committed to serving clients with best practices to help them with change, improvements and better quality of life. We believe in providing a secure, supportive environment to grow as a clinician and learn how to foster longevity in the career which is part of our mission statement. Thank you for taking the time to explore a career with us. We are excited to be a new group practice in the community. If you are looking for quality supervision as you work towards licensure and ability to serve populations while accepting a variety of insurance panels, we may be a good fit. Our supervisors are trained in EMDR and utilize a parts work perspective with a trauma lens. We are actively looking to hire a therapist in the area who is passionate about working with adults and committed to growth and excellence in the field. We are located in Old Town Square, Fort Collins. We value and are strengthened by diversity and desire a warm and welcoming place for all people. We believe in racial and ethnic equality, gender equity and social inclusion. Position Requirement Possibilities:A graduate level psychological counseling-related degreeMasters of Social Work (MSW/LSW)Licensed Professional Counselor Candidate (LPCC)Clinical Social worker (LCSW)Professional Counselor (LPC)Marriage/Family Therapist (LMFT)Relating to this?Wanting to deliver high quality mental healthcareSeeking quality supervision and growth in a healthy environmentWhat we offer:Flexible work scheduleW2 Employment - commission basedBuilding to full time workJump of 5% in commission as well as monthly bonus/stipend once full timeWeekly supervision providedPaid weekly team meetings $30/hrTwo paid wellness hours/month $30/hrTelemedicine and in-person flexibilitySupportive work environment with direct access to two supervisorsAdministrative supportApproved professional development training providedFully automated EHR and technology supportStrong work/life balanceJob Duties:Conducting intake assessmentsDeveloping and implementing treatment plans for clients based on assessment and coordinating any additional services needed, revising as necessaryConducting individual sessions as appropriate for the treatment plan of the patientApplying psychotherapeutic techniques and interventions in the delivery of services to individuals for the purpose of treating emotional and behavioral disorders that have been diagnosed in assessmentParticipating in team meetings in order to staff new cases. Presenting appropriate patient information to the team. Recommending effective treatment interventions.Building and maintaining an active caseload with assigned clientsCompleting timely progress notes and treatment updates in the EHR. Maintaining all clinical documentation in accordance with regulatory and accrediting standardsProviding crisis intervention to patients in acute distress and referring as neededPerforming case management and discharge planning as neededExcellent communication and interpersonal skillsCompassionate and empathetic approach to patient carePlease send resume and cover letter to info@aspentherapyandwellness.com About Aspen Therapy and Wellness LLCAspen Therapy and Wellness is a mental health services provider focusing on work with adults in an outpatient setting, working with a variety of mental health issues both in-person in Old Town Fort Collins and throughout the state of Colorado via telehealth services. Please note that this job description is not exhaustive and additional duties may be assigned as needed.
3rd rowThe National Exemplar is accepting applications for an Assistant Restaurant Manager. We offer highly competitive wages, healthcare, paid time off, complimentary dining privileges and bonus opportunities. We are a serious, professional, long-standing neighborhood restaurant with over 41 years of service. If you are looking for a long-term fit with a best in class organization then you should apply now. Please send a resumes to pardom@nationalexemplar.com. o
4th rowSenior Associate Attorney - Elder Law / Trusts and Estates Our legal team is committed to providing each client with quality counsel, innovative solutions, and personalized service. Founded in 2000, the firm offers the legal expertise of its 115+ attorneys, who have accumulated experience and problem-solving skills over decades of practice. We are a prominent Lake Success Law Firm seeking an associate attorney for its growing Elder Law and Estate Planning practice. The successful candidate will be a self-motivated, detail-oriented team member with strong communication skills and a desire to grow their practice. Experience with Estate Planning, Administration, and Litigation and is preferred. Responsibilities will include: Counseling clients with regard to estate planning and asset protection;Formulating and overseeing execution of Medicaid and estate plans;Drafting wills, revocable and irrevocable trusts, powers of attorney, health care proxies, and living wills;Estate Administration;Trust Administration;Court Appearances for Estate and Proceedings;Supervising paralegals Qualifications:Juris Doctor degree (J.D.) from an accredited law schoolLicensed to practice law in New York10-15 years of experienceExperience with various advance directives, trusts, and willsStrong analytical and problem-solving skillsAbility to build rapport with clientsExcellent written and verbal communication skills Competitive salary commensurate with experienceSalary: $140,000- $175,000Benefits: 401k, Medical, Dental, Life Insurance, PTO, and more This position is based out of Lake Success, NY
5th rowLooking for HVAC service tech with experience in commerical and industrial equipment. Minimum 5 yrs. on the job with mechanical license. Winger is a full line union mechanical business with Piping, plumbing, sheet metal and service.
ValueCountFrequency (%)
and 3747841
 
5.8%
to 2056924
 
3.2%
the 1800301
 
2.8%
of 1475766
 
2.3%
a 1107792
 
1.7%
in 1045235
 
1.6%
with 858095
 
1.3%
for 807125
 
1.2%
or 560386
 
0.9%
is 499949
 
0.8%
Other values (857345) 50817072
78.4%
2024-10-17T22:09:02.871418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63441435
13.6%
e 44512658
 
9.5%
i 32390472
 
6.9%
n 30399658
 
6.5%
t 30356083
 
6.5%
a 30078361
 
6.4%
o 27952007
 
6.0%
r 25317925
 
5.4%
s 24418713
 
5.2%
l 16904821
 
3.6%
Other values (1847) 140674327
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 466446460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
63441435
13.6%
e 44512658
 
9.5%
i 32390472
 
6.9%
n 30399658
 
6.5%
t 30356083
 
6.5%
a 30078361
 
6.4%
o 27952007
 
6.0%
r 25317925
 
5.4%
s 24418713
 
5.2%
l 16904821
 
3.6%
Other values (1847) 140674327
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 466446460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
63441435
13.6%
e 44512658
 
9.5%
i 32390472
 
6.9%
n 30399658
 
6.5%
t 30356083
 
6.5%
a 30078361
 
6.4%
o 27952007
 
6.0%
r 25317925
 
5.4%
s 24418713
 
5.2%
l 16904821
 
3.6%
Other values (1847) 140674327
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 466446460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
63441435
13.6%
e 44512658
 
9.5%
i 32390472
 
6.9%
n 30399658
 
6.5%
t 30356083
 
6.5%
a 30078361
 
6.4%
o 27952007
 
6.0%
r 25317925
 
5.4%
s 24418713
 
5.2%
l 16904821
 
3.6%
Other values (1847) 140674327
30.2%

max_salary
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct5321
Distinct (%)17.9%
Missing94056
Missing (%)75.9%
Infinite0
Infinite (%)0.0%
Mean91939.423
Minimum1
Maximum1.2 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:02.942520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q148.28
median80000
Q3140000
95-th percentile245000
Maximum1.2 × 108
Range1.2 × 108
Interquartile range (IQR)139951.72

Descriptive statistics

Standard deviation701110.14
Coefficient of variation (CV)7.6257835
Kurtosis28720.438
Mean91939.423
Median Absolute Deviation (MAD)79930
Skewness167.93071
Sum2.7391512 × 109
Variance4.9155543 × 1011
MonotonicityNot monotonic
2024-10-17T22:09:02.997134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 648
 
0.5%
100000 635
 
0.5%
120000 610
 
0.5%
90000 541
 
0.4%
85000 534
 
0.4%
110000 503
 
0.4%
80000 489
 
0.4%
75000 459
 
0.4%
130000 446
 
0.4%
70000 422
 
0.3%
Other values (5311) 24506
 
19.8%
(Missing) 94056
75.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
7 1
< 0.1%
7.25 1
< 0.1%
7.8 2
< 0.1%
8.98 1
< 0.1%
10 1
< 0.1%
10.8 1
< 0.1%
11 2
< 0.1%
11.25 1
< 0.1%
ValueCountFrequency (%)
120000000 1
 
< 0.1%
1500000 4
 
< 0.1%
1300000 1
 
< 0.1%
1220000 1
 
< 0.1%
1200000 1
 
< 0.1%
1115000 1
 
< 0.1%
1100000 1
 
< 0.1%
1000001 1
 
< 0.1%
1000000 10
< 0.1%
998426 1
 
< 0.1%

pay_period
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing87776
Missing (%)70.9%
Memory size967.7 KiB
YEARLY
20628 
HOURLY
14741 
MONTHLY
 
518
WEEKLY
 
177
BIWEEKLY
 
9

Length

Max length8
Median length6
Mean length6.0148588
Min length6

Characters and Unicode

Total characters216974
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOURLY
2nd rowHOURLY
3rd rowYEARLY
4th rowYEARLY
5th rowYEARLY

Common Values

ValueCountFrequency (%)
YEARLY 20628
 
16.7%
HOURLY 14741
 
11.9%
MONTHLY 518
 
0.4%
WEEKLY 177
 
0.1%
BIWEEKLY 9
 
< 0.1%
(Missing) 87776
70.9%

Length

2024-10-17T22:09:03.052874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:03.102250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
yearly 20628
57.2%
hourly 14741
40.9%
monthly 518
 
1.4%
weekly 177
 
0.5%
biweekly 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Y 56701
26.1%
L 36073
16.6%
R 35369
16.3%
E 21000
 
9.7%
A 20628
 
9.5%
H 15259
 
7.0%
O 15259
 
7.0%
U 14741
 
6.8%
M 518
 
0.2%
N 518
 
0.2%
Other values (5) 908
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 216974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 56701
26.1%
L 36073
16.6%
R 35369
16.3%
E 21000
 
9.7%
A 20628
 
9.5%
H 15259
 
7.0%
O 15259
 
7.0%
U 14741
 
6.8%
M 518
 
0.2%
N 518
 
0.2%
Other values (5) 908
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 216974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 56701
26.1%
L 36073
16.6%
R 35369
16.3%
E 21000
 
9.7%
A 20628
 
9.5%
H 15259
 
7.0%
O 15259
 
7.0%
U 14741
 
6.8%
M 518
 
0.2%
N 518
 
0.2%
Other values (5) 908
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 216974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 56701
26.1%
L 36073
16.6%
R 35369
16.3%
E 21000
 
9.7%
A 20628
 
9.5%
H 15259
 
7.0%
O 15259
 
7.0%
U 14741
 
6.8%
M 518
 
0.2%
N 518
 
0.2%
Other values (5) 908
 
0.4%
Distinct8526
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:03.235607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length56
Median length52
Mean length13.937416
Min length4

Characters and Unicode

Total characters1726135
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2936 ?
Unique (%)2.4%

Sample

1st rowPrinceton, NJ
2nd rowFort Collins, CO
3rd rowCincinnati, OH
4th rowNew Hyde Park, NY
5th rowBurlington, IA
ValueCountFrequency (%)
united 12702
 
4.3%
states 12695
 
4.3%
ca 11484
 
3.9%
tx 10271
 
3.5%
ny 6044
 
2.1%
fl 5907
 
2.0%
new 5561
 
1.9%
nc 4928
 
1.7%
area 4850
 
1.6%
il 4486
 
1.5%
Other values (5204) 215225
73.2%
2024-10-17T22:09:03.440119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
170304
 
9.9%
e 125109
 
7.2%
a 116916
 
6.8%
, 111146
 
6.4%
t 104612
 
6.1%
n 96975
 
5.6%
o 90809
 
5.3%
i 81754
 
4.7%
r 71238
 
4.1%
l 67892
 
3.9%
Other values (52) 689380
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1726135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
170304
 
9.9%
e 125109
 
7.2%
a 116916
 
6.8%
, 111146
 
6.4%
t 104612
 
6.1%
n 96975
 
5.6%
o 90809
 
5.3%
i 81754
 
4.7%
r 71238
 
4.1%
l 67892
 
3.9%
Other values (52) 689380
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1726135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
170304
 
9.9%
e 125109
 
7.2%
a 116916
 
6.8%
, 111146
 
6.4%
t 104612
 
6.1%
n 96975
 
5.6%
o 90809
 
5.3%
i 81754
 
4.7%
r 71238
 
4.1%
l 67892
 
3.9%
Other values (52) 689380
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1726135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
170304
 
9.9%
e 125109
 
7.2%
a 116916
 
6.8%
, 111146
 
6.4%
t 104612
 
6.1%
n 96975
 
5.6%
o 90809
 
5.3%
i 81754
 
4.7%
r 71238
 
4.1%
l 67892
 
3.9%
Other values (52) 689380
39.9%

company_id
Real number (ℝ)

MISSING 

Distinct24474
Distinct (%)20.0%
Missing1717
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean12204012
Minimum1009
Maximum1.0347298 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:03.509193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2017
Q114352
median226965
Q38047188
95-th percentile80515234
Maximum1.0347298 × 108
Range1.0347197 × 108
Interquartile range (IQR)8032836

Descriptive statistics

Standard deviation25541432
Coefficient of variation (CV)2.0928717
Kurtosis3.8128193
Mean12204012
Median Absolute Deviation (MAD)225353
Skewness2.2623321
Sum1.4905004 × 1012
Variance6.5236473 × 1014
MonotonicityNot monotonic
2024-10-17T22:09:03.562627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53345529 1108
 
0.9%
167757 1003
 
0.8%
73013724 604
 
0.5%
2152 529
 
0.4%
4128 527
 
0.4%
3175 517
 
0.4%
1419 496
 
0.4%
163139 476
 
0.4%
11056 418
 
0.3%
6849 415
 
0.3%
Other values (24464) 116039
93.7%
(Missing) 1717
 
1.4%
ValueCountFrequency (%)
1009 33
 
< 0.1%
1016 53
< 0.1%
1025 14
 
< 0.1%
1028 93
0.1%
1033 20
 
< 0.1%
1035 62
0.1%
1038 101
0.1%
1043 54
< 0.1%
1044 32
 
< 0.1%
1052 55
< 0.1%
ValueCountFrequency (%)
103472979 1
< 0.1%
103468936 1
< 0.1%
103467540 1
< 0.1%
103466352 1
< 0.1%
103463217 1
< 0.1%
103458790 1
< 0.1%
103456527 1
< 0.1%
103456466 1
< 0.1%
103455555 1
< 0.1%
103454653 1
< 0.1%

views
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct684
Distinct (%)0.6%
Missing1689
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean14.618247
Minimum1
Maximum9975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:03.613534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile48
Maximum9975
Range9974
Interquartile range (IQR)5

Descriptive statistics

Standard deviation85.903598
Coefficient of variation (CV)5.8764639
Kurtosis4603.8319
Mean14.618247
Median Absolute Deviation (MAD)2
Skewness53.72544
Sum1785765
Variance7379.4281
MonotonicityNot monotonic
2024-10-17T22:09:03.664792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 28323
22.9%
3 16960
13.7%
5 14513
11.7%
2 14417
11.6%
6 7834
 
6.3%
7 4922
 
4.0%
1 4587
 
3.7%
8 3376
 
2.7%
9 2472
 
2.0%
10 1926
 
1.6%
Other values (674) 22830
18.4%
(Missing) 1689
 
1.4%
ValueCountFrequency (%)
1 4587
 
3.7%
2 14417
11.6%
3 16960
13.7%
4 28323
22.9%
5 14513
11.7%
6 7834
 
6.3%
7 4922
 
4.0%
8 3376
 
2.7%
9 2472
 
2.0%
10 1926
 
1.6%
ValueCountFrequency (%)
9975 1
< 0.1%
9949 1
< 0.1%
8062 1
< 0.1%
6105 1
< 0.1%
5518 1
< 0.1%
5132 1
< 0.1%
4951 1
< 0.1%
4807 1
< 0.1%
4753 1
< 0.1%
4378 1
< 0.1%

med_salary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1417
Distinct (%)22.6%
Missing117569
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean22015.62
Minimum0
Maximum750000
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:03.921847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q118.94
median25.5
Q32510.5
95-th percentile120000
Maximum750000
Range750000
Interquartile range (IQR)2491.56

Descriptive statistics

Standard deviation52255.874
Coefficient of variation (CV)2.3735818
Kurtosis34.168445
Mean22015.62
Median Absolute Deviation (MAD)9.5
Skewness4.498174
Sum1.3825809 × 108
Variance2.7306764 × 109
MonotonicityNot monotonic
2024-10-17T22:09:03.973537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 268
 
0.2%
18 243
 
0.2%
15 177
 
0.1%
17 169
 
0.1%
16 158
 
0.1%
25 140
 
0.1%
19 137
 
0.1%
21 114
 
0.1%
22 107
 
0.1%
23 95
 
0.1%
Other values (1407) 4672
 
3.8%
(Missing) 117569
94.9%
ValueCountFrequency (%)
0 14
< 0.1%
9 1
 
< 0.1%
10 5
 
< 0.1%
10.75 1
 
< 0.1%
11 13
< 0.1%
11.25 1
 
< 0.1%
11.29 1
 
< 0.1%
11.5 5
 
< 0.1%
11.75 5
 
< 0.1%
12 26
< 0.1%
ValueCountFrequency (%)
750000 2
< 0.1%
680000 1
 
< 0.1%
550000 1
 
< 0.1%
541000 1
 
< 0.1%
525000 1
 
< 0.1%
500000 4
< 0.1%
488668 2
< 0.1%
450000 1
 
< 0.1%
425000 1
 
< 0.1%
400000 2
< 0.1%

min_salary
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct4612
Distinct (%)15.5%
Missing94056
Missing (%)75.9%
Infinite0
Infinite (%)0.0%
Mean64910.847
Minimum1
Maximum85000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:04.025370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.7
Q137
median60000
Q3100000
95-th percentile166080
Maximum85000000
Range84999999
Interquartile range (IQR)99963

Descriptive statistics

Standard deviation495973.79
Coefficient of variation (CV)7.6408461
Kurtosis28870.638
Mean64910.847
Median Absolute Deviation (MAD)59935
Skewness168.58568
Sum1.9338889 × 109
Variance2.4599 × 1011
MonotonicityNot monotonic
2024-10-17T22:09:04.076815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 873
 
0.7%
80000 841
 
0.7%
90000 766
 
0.6%
70000 752
 
0.6%
60000 676
 
0.5%
20 615
 
0.5%
120000 575
 
0.5%
75000 575
 
0.5%
65000 572
 
0.5%
50000 509
 
0.4%
Other values (4602) 23039
 
18.6%
(Missing) 94056
75.9%
ValueCountFrequency (%)
1 13
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
7.25 3
 
< 0.1%
7.8 2
 
< 0.1%
8.98 1
 
< 0.1%
10 44
< 0.1%
10.45 2
 
< 0.1%
10.5 2
 
< 0.1%
10.6 2
 
< 0.1%
ValueCountFrequency (%)
85000000 1
 
< 0.1%
750000 3
 
< 0.1%
700000 1
 
< 0.1%
650000 1
 
< 0.1%
600000 2
 
< 0.1%
575000 1
 
< 0.1%
560000 1
 
< 0.1%
550000 1
 
< 0.1%
540000 1
 
< 0.1%
500000 11
< 0.1%

formatted_work_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size967.7 KiB
Full-time
98814 
Contract
12117 
Part-time
 
9696
Temporary
 
1190
Internship
 
983
Other values (2)
 
1049

Length

Max length10
Median length9
Mean length8.8943714
Min length5

Characters and Unicode

Total characters1101559
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFull-time
2nd rowFull-time
3rd rowFull-time
4th rowFull-time
5th rowFull-time

Common Values

ValueCountFrequency (%)
Full-time 98814
79.8%
Contract 12117
 
9.8%
Part-time 9696
 
7.8%
Temporary 1190
 
1.0%
Internship 983
 
0.8%
Volunteer 562
 
0.5%
Other 487
 
0.4%

Length

2024-10-17T22:09:04.123694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:04.164178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
full-time 98814
79.8%
contract 12117
 
9.8%
part-time 9696
 
7.8%
temporary 1190
 
1.0%
internship 983
 
0.8%
volunteer 562
 
0.5%
other 487
 
0.4%

Most occurring characters

ValueCountFrequency (%)
l 198190
18.0%
t 144472
13.1%
e 112294
10.2%
m 109700
10.0%
i 109493
9.9%
- 108510
9.9%
u 99376
9.0%
F 98814
9.0%
r 26225
 
2.4%
a 23003
 
2.1%
Other values (13) 71482
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1101559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 198190
18.0%
t 144472
13.1%
e 112294
10.2%
m 109700
10.0%
i 109493
9.9%
- 108510
9.9%
u 99376
9.0%
F 98814
9.0%
r 26225
 
2.4%
a 23003
 
2.1%
Other values (13) 71482
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1101559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 198190
18.0%
t 144472
13.1%
e 112294
10.2%
m 109700
10.0%
i 109493
9.9%
- 108510
9.9%
u 99376
9.0%
F 98814
9.0%
r 26225
 
2.4%
a 23003
 
2.1%
Other values (13) 71482
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1101559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 198190
18.0%
t 144472
13.1%
e 112294
10.2%
m 109700
10.0%
i 109493
9.9%
- 108510
9.9%
u 99376
9.0%
F 98814
9.0%
r 26225
 
2.4%
a 23003
 
2.1%
Other values (13) 71482
 
6.5%

applies
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct274
Distinct (%)1.2%
Missing100529
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean10.591981
Minimum1
Maximum967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:04.214808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile45
Maximum967
Range966
Interquartile range (IQR)7

Descriptive statistics

Standard deviation29.047395
Coefficient of variation (CV)2.7423949
Kurtosis147.95358
Mean10.591981
Median Absolute Deviation (MAD)2
Skewness9.4359854
Sum247005
Variance843.75113
MonotonicityNot monotonic
2024-10-17T22:09:04.265224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7830
 
6.3%
2 3351
 
2.7%
3 2053
 
1.7%
4 1395
 
1.1%
5 1108
 
0.9%
6 840
 
0.7%
7 625
 
0.5%
8 530
 
0.4%
10 419
 
0.3%
9 418
 
0.3%
Other values (264) 4751
 
3.8%
(Missing) 100529
81.2%
ValueCountFrequency (%)
1 7830
6.3%
2 3351
2.7%
3 2053
 
1.7%
4 1395
 
1.1%
5 1108
 
0.9%
6 840
 
0.7%
7 625
 
0.5%
8 530
 
0.4%
9 418
 
0.3%
10 419
 
0.3%
ValueCountFrequency (%)
967 1
< 0.1%
729 1
< 0.1%
625 1
< 0.1%
566 1
< 0.1%
530 1
< 0.1%
508 1
< 0.1%
493 1
< 0.1%
472 1
< 0.1%
470 1
< 0.1%
465 1
< 0.1%

original_listed_time
Real number (ℝ)

HIGH CORRELATION 

Distinct65036
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7131523 × 1012
Minimum1.7018105 × 1012
Maximum1.7135728 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:04.313327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.7018105 × 1012
5-th percentile1.7123486 × 1012
Q11.7128629 × 1012
median1.713395 × 1012
Q31.7134783 × 1012
95-th percentile1.7135614 × 1012
Maximum1.7135728 × 1012
Range1.176227 × 1010
Interquartile range (IQR)6.15448 × 108

Descriptive statistics

Standard deviation4.8482088 × 108
Coefficient of variation (CV)0.00028299928
Kurtosis15.229625
Mean1.7131523 × 1012
Median Absolute Deviation (MAD)1.3972 × 108
Skewness-2.322499
Sum2.121722 × 1017
Variance2.3505128 × 1017
MonotonicityNot monotonic
2024-10-17T22:09:04.368405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7133984 × 10126788
 
5.5%
1.713312 × 10123979
 
3.2%
1.7134848 × 10123373
 
2.7%
1.7122752 × 10121975
 
1.6%
1.7127936 × 10121966
 
1.6%
1.7125344 × 10121229
 
1.0%
1.7131392 × 10121120
 
0.9%
1.7132256 × 1012607
 
0.5%
1.7126208 × 1012442
 
0.4%
1.71288 × 1012239
 
0.2%
Other values (65026) 102131
82.5%
ValueCountFrequency (%)
1.701810533 × 10121
< 0.1%
1.702050434 × 10121
< 0.1%
1.703184555 × 10121
< 0.1%
1.704485921 × 10121
< 0.1%
1.704485944 × 10121
< 0.1%
1.704736739 × 10121
< 0.1%
1.705371927 × 10121
< 0.1%
1.7060544 × 10121
< 0.1%
1.706240402 × 10121
< 0.1%
1.706305637 × 10121
< 0.1%
ValueCountFrequency (%)
1.713572803 × 10121
< 0.1%
1.71357279 × 10121
< 0.1%
1.713572788 × 10121
< 0.1%
1.713572768 × 10121
< 0.1%
1.713572767 × 10121
< 0.1%
1.713572765 × 10121
< 0.1%
1.713572738 × 10121
< 0.1%
1.713572703 × 10121
< 0.1%
1.713572687 × 10121
< 0.1%
1.713572679 × 10121
< 0.1%

remote_allowed
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing108603
Missing (%)87.7%
Memory size967.7 KiB
1.0
15246 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45738
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15246
 
12.3%
(Missing) 108603
87.7%

Length

2024-10-17T22:09:04.416122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:04.450613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15246
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15246
33.3%
. 15246
33.3%
0 15246
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15246
33.3%
. 15246
33.3%
0 15246
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15246
33.3%
. 15246
33.3%
0 15246
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15246
33.3%
. 15246
33.3%
0 15246
33.3%

job_posting_url
Text

UNIQUE 

Distinct123849
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:04.639526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length69
Median length69
Mean length68.999419
Min length65

Characters and Unicode

Total characters8545509
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123849 ?
Unique (%)100.0%

Sample

1st rowhttps://www.linkedin.com/jobs/view/921716/?trk=jobs_biz_prem_srch
2nd rowhttps://www.linkedin.com/jobs/view/1829192/?trk=jobs_biz_prem_srch
3rd rowhttps://www.linkedin.com/jobs/view/10998357/?trk=jobs_biz_prem_srch
4th rowhttps://www.linkedin.com/jobs/view/23221523/?trk=jobs_biz_prem_srch
5th rowhttps://www.linkedin.com/jobs/view/35982263/?trk=jobs_biz_prem_srch
ValueCountFrequency (%)
https://www.linkedin.com/jobs/view/921716/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/243731357/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/35982263/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/91700727/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/103254301/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/112576855/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/1218575/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/2264355/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/9615617/?trk=jobs_biz_prem_srch 1
 
< 0.1%
https://www.linkedin.com/jobs/view/11009123/?trk=jobs_biz_prem_srch 1
 
< 0.1%
Other values (123839) 123839
> 99.9%
2024-10-17T22:09:04.900627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 743094
 
8.7%
s 495396
 
5.8%
w 495396
 
5.8%
i 495396
 
5.8%
_ 371547
 
4.3%
r 371547
 
4.3%
b 371547
 
4.3%
t 371547
 
4.3%
o 371547
 
4.3%
e 371547
 
4.3%
Other values (25) 4086945
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8545509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 743094
 
8.7%
s 495396
 
5.8%
w 495396
 
5.8%
i 495396
 
5.8%
_ 371547
 
4.3%
r 371547
 
4.3%
b 371547
 
4.3%
t 371547
 
4.3%
o 371547
 
4.3%
e 371547
 
4.3%
Other values (25) 4086945
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8545509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 743094
 
8.7%
s 495396
 
5.8%
w 495396
 
5.8%
i 495396
 
5.8%
_ 371547
 
4.3%
r 371547
 
4.3%
b 371547
 
4.3%
t 371547
 
4.3%
o 371547
 
4.3%
e 371547
 
4.3%
Other values (25) 4086945
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8545509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 743094
 
8.7%
s 495396
 
5.8%
w 495396
 
5.8%
i 495396
 
5.8%
_ 371547
 
4.3%
r 371547
 
4.3%
b 371547
 
4.3%
t 371547
 
4.3%
o 371547
 
4.3%
e 371547
 
4.3%
Other values (25) 4086945
47.8%

application_url
Text

MISSING 

Distinct84800
Distinct (%)97.3%
Missing36665
Missing (%)29.6%
Memory size967.7 KiB
2024-10-17T22:09:05.156277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length555
Median length454
Mean length118.48887
Min length9

Characters and Unicode

Total characters10330334
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83684 ?
Unique (%)96.0%

Sample

1st rowwww.childrensnebraska.org
2nd rowhttps://www.kidsbookbank.org/employment/
3rd rowhttps://wsu.wd5.myworkdayjobs.com/en-US/WSU_Jobs/job/Coordinator-for-Multicultural-Student-Organizations_R-11504?source=Linkedin
4th rowhttps://www.indeed.com/job/embryologist-944f8c0533634448
5th rowhttps://jobs.tmcaz.com/manager-pharmacy-retail/job/27853271
ValueCountFrequency (%)
https://app.dataannotation.tech/worker_signup?projects=prog_sa&worker_src=l&utm_medium=display&utm_source=linkedin&utm_campaign=softwaredeveloper 205
 
0.2%
https://revature.com/jobs/entry-level-automotive-engineer/?utm_source=linkedin&sourcedby=balalp 67
 
0.1%
https://app.dataannotation.tech/worker_signup?worker_src=li&utm_medium=display&utm_source=linkedin&utm_campaign=aicontentwriter 60
 
0.1%
https://www.english1.com/apply-now/?utm_source=linkedin&utm_medium=listing&utm_term=china&utm_content=linkedin&utm_campaign=linkedin 44
 
0.1%
https://optimabiosupply.com/job-opening-data-entry-clerk 43
 
< 0.1%
https://dataforcecommunity.transperfect.com/project/amethyst-project-us?/fill?id313=df017&id315=davids 18
 
< 0.1%
https://forms.gle/lj3m5jhm2xwtza1h6 17
 
< 0.1%
https://forms.office.com/r/uavry128aw 15
 
< 0.1%
https://hourlyjobs-us.mondelezinternational.com/?src=sns-12680 15
 
< 0.1%
https://revature.com/jobs/entry-level-oracle-financial-technology/?utm_source=linkedin&sourcedby=balalp 12
 
< 0.1%
Other values (84788) 86688
99.4%
2024-10-17T22:09:05.452282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 650037
 
6.3%
t 527392
 
5.1%
o 503223
 
4.9%
s 472084
 
4.6%
/ 468425
 
4.5%
r 442929
 
4.3%
i 434232
 
4.2%
a 408247
 
4.0%
n 388888
 
3.8%
c 381655
 
3.7%
Other values (74) 5653222
54.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10330334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 650037
 
6.3%
t 527392
 
5.1%
o 503223
 
4.9%
s 472084
 
4.6%
/ 468425
 
4.5%
r 442929
 
4.3%
i 434232
 
4.2%
a 408247
 
4.0%
n 388888
 
3.8%
c 381655
 
3.7%
Other values (74) 5653222
54.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10330334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 650037
 
6.3%
t 527392
 
5.1%
o 503223
 
4.9%
s 472084
 
4.6%
/ 468425
 
4.5%
r 442929
 
4.3%
i 434232
 
4.2%
a 408247
 
4.0%
n 388888
 
3.8%
c 381655
 
3.7%
Other values (74) 5653222
54.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10330334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 650037
 
6.3%
t 527392
 
5.1%
o 503223
 
4.9%
s 472084
 
4.6%
/ 468425
 
4.5%
r 442929
 
4.3%
i 434232
 
4.2%
a 408247
 
4.0%
n 388888
 
3.8%
c 381655
 
3.7%
Other values (74) 5653222
54.7%

application_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size967.7 KiB
OffsiteApply
84607 
ComplexOnsiteApply
31049 
SimpleOnsiteApply
 
8192
UnknownApply
 
1

Length

Max length18
Median length12
Mean length13.834928
Min length12

Characters and Unicode

Total characters1713442
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowComplexOnsiteApply
2nd rowComplexOnsiteApply
3rd rowComplexOnsiteApply
4th rowComplexOnsiteApply
5th rowComplexOnsiteApply

Common Values

ValueCountFrequency (%)
OffsiteApply 84607
68.3%
ComplexOnsiteApply 31049
 
25.1%
SimpleOnsiteApply 8192
 
6.6%
UnknownApply 1
 
< 0.1%

Length

2024-10-17T22:09:05.502777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:05.541302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
offsiteapply 84607
68.3%
complexonsiteapply 31049
 
25.1%
simpleonsiteapply 8192
 
6.6%
unknownapply 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
p 286939
16.7%
f 169214
9.9%
l 163090
9.5%
e 163089
9.5%
i 132040
7.7%
y 123849
7.2%
A 123849
7.2%
O 123848
7.2%
t 123848
7.2%
s 123848
7.2%
Other values (9) 179828
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1713442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 286939
16.7%
f 169214
9.9%
l 163090
9.5%
e 163089
9.5%
i 132040
7.7%
y 123849
7.2%
A 123849
7.2%
O 123848
7.2%
t 123848
7.2%
s 123848
7.2%
Other values (9) 179828
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1713442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 286939
16.7%
f 169214
9.9%
l 163090
9.5%
e 163089
9.5%
i 132040
7.7%
y 123849
7.2%
A 123849
7.2%
O 123848
7.2%
t 123848
7.2%
s 123848
7.2%
Other values (9) 179828
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1713442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 286939
16.7%
f 169214
9.9%
l 163090
9.5%
e 163089
9.5%
i 132040
7.7%
y 123849
7.2%
A 123849
7.2%
O 123848
7.2%
t 123848
7.2%
s 123848
7.2%
Other values (9) 179828
10.5%

expiry
Real number (ℝ)

HIGH CORRELATION 

Distinct54851
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.716213 × 1012
Minimum1.7129034 × 1012
Maximum1.7291248 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:05.587215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.7129034 × 1012
5-th percentile1.7149611 × 1012
Q11.7154807 × 1012
median1.7160423 × 1012
Q31.7160882 × 1012
95-th percentile1.7161594 × 1012
Maximum1.7291248 × 1012
Range1.6221348 × 1010
Interquartile range (IQR)6.07511 × 108

Descriptive statistics

Standard deviation2.3213939 × 109
Coefficient of variation (CV)0.0013526257
Kurtosis24.403668
Mean1.716213 × 1012
Median Absolute Deviation (MAD)1.04672 × 108
Skewness5.0361432
Sum2.1255127 × 1017
Variance5.3888696 × 1018
MonotonicityNot monotonic
2024-10-17T22:09:05.639519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.714966714 × 1012123
 
0.1%
1.716110539 × 101296
 
0.1%
1.716110364 × 101294
 
0.1%
1.716085196 × 101291
 
0.1%
1.715869799 × 101289
 
0.1%
1.715477166 × 101288
 
0.1%
1.716064823 × 101288
 
0.1%
1.71548865 × 101287
 
0.1%
1.715246494 × 101285
 
0.1%
1.715986284 × 101285
 
0.1%
Other values (54841) 122923
99.3%
ValueCountFrequency (%)
1.712903448 × 10121
< 0.1%
1.712903618 × 10121
< 0.1%
1.712904325 × 10121
< 0.1%
1.712953546 × 10121
< 0.1%
1.713066163 × 10121
< 0.1%
1.713074495 × 10121
< 0.1%
1.713074501 × 10121
< 0.1%
1.713162836 × 10121
< 0.1%
1.713226875 × 10121
< 0.1%
1.713265275 × 10121
< 0.1%
ValueCountFrequency (%)
1.729124796 × 10121
< 0.1%
1.729124746 × 10121
< 0.1%
1.729124673 × 10121
< 0.1%
1.729124637 × 10121
< 0.1%
1.729124518 × 10121
< 0.1%
1.72912446 × 10121
< 0.1%
1.729124291 × 10121
< 0.1%
1.729124274 × 10121
< 0.1%
1.729124017 × 10121
< 0.1%
1.729123972 × 10121
< 0.1%

closed_time
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct698
Distinct (%)65.1%
Missing122776
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean1.7129279 × 1012
Minimum1.7123459 × 1012
Maximum1.7135621 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:05.693116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.7123459 × 1012
5-th percentile1.7123882 × 1012
Q11.7126698 × 1012
median1.7126699 × 1012
Q31.7132826 × 1012
95-th percentile1.713463 × 1012
Maximum1.7135621 × 1012
Range1.216175 × 109
Interquartile range (IQR)6.12809 × 108

Descriptive statistics

Standard deviation3.6228935 × 108
Coefficient of variation (CV)0.00021150298
Kurtosis-1.4293138
Mean1.7129279 × 1012
Median Absolute Deviation (MAD)1106000
Skewness0.40650159
Sum1.8379716 × 1015
Variance1.3125357 × 1017
MonotonicityNot monotonic
2024-10-17T22:09:05.747520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.712669775 × 101215
 
< 0.1%
1.71266969 × 101215
 
< 0.1%
1.712669839 × 101214
 
< 0.1%
1.71266984 × 101214
 
< 0.1%
1.712669778 × 101211
 
< 0.1%
1.712669848 × 101210
 
< 0.1%
1.712669762 × 10129
 
< 0.1%
1.712669893 × 10128
 
< 0.1%
1.712669939 × 10128
 
< 0.1%
1.712669886 × 10127
 
< 0.1%
Other values (688) 962
 
0.8%
(Missing) 122776
99.1%
ValueCountFrequency (%)
1.712345932 × 10121
< 0.1%
1.712347012 × 10121
< 0.1%
1.712347107 × 10121
< 0.1%
1.71234763 × 10121
< 0.1%
1.712347879 × 10121
< 0.1%
1.712348074 × 10121
< 0.1%
1.712348391 × 10121
< 0.1%
1.712349766 × 10121
< 0.1%
1.71234986 × 10121
< 0.1%
1.712350094 × 10121
< 0.1%
ValueCountFrequency (%)
1.713562107 × 10121
< 0.1%
1.713556726 × 10121
< 0.1%
1.713556566 × 10121
< 0.1%
1.713556548 × 10121
< 0.1%
1.713556463 × 10121
< 0.1%
1.713556377 × 10121
< 0.1%
1.713555239 × 10121
< 0.1%
1.713554784 × 10121
< 0.1%
1.713554061 × 10121
< 0.1%
1.713554013 × 10121
< 0.1%

formatted_experience_level
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing29409
Missing (%)23.7%
Memory size967.7 KiB
Mid-Senior level
41489 
Entry level
36708 
Associate
9826 
Director
 
3746
Internship
 
1449

Length

Max length16
Median length11
Mean length12.828272
Min length8

Characters and Unicode

Total characters1211502
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntry level
2nd rowMid-Senior level
3rd rowMid-Senior level
4th rowAssociate
5th rowEntry level

Common Values

ValueCountFrequency (%)
Mid-Senior level 41489
33.5%
Entry level 36708
29.6%
Associate 9826
 
7.9%
Director 3746
 
3.0%
Internship 1449
 
1.2%
Executive 1222
 
1.0%
(Missing) 29409
23.7%

Length

2024-10-17T22:09:05.800638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:05.842087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
level 78197
45.3%
mid-senior 41489
24.0%
entry 36708
21.3%
associate 9826
 
5.7%
director 3746
 
2.2%
internship 1449
 
0.8%
executive 1222
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e 215348
17.8%
l 156394
12.9%
i 99221
 
8.2%
r 87138
 
7.2%
n 81095
 
6.7%
v 79419
 
6.6%
78197
 
6.5%
o 55061
 
4.5%
t 52951
 
4.4%
M 41489
 
3.4%
Other values (15) 265189
21.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1211502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 215348
17.8%
l 156394
12.9%
i 99221
 
8.2%
r 87138
 
7.2%
n 81095
 
6.7%
v 79419
 
6.6%
78197
 
6.5%
o 55061
 
4.5%
t 52951
 
4.4%
M 41489
 
3.4%
Other values (15) 265189
21.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1211502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 215348
17.8%
l 156394
12.9%
i 99221
 
8.2%
r 87138
 
7.2%
n 81095
 
6.7%
v 79419
 
6.6%
78197
 
6.5%
o 55061
 
4.5%
t 52951
 
4.4%
M 41489
 
3.4%
Other values (15) 265189
21.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1211502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 215348
17.8%
l 156394
12.9%
i 99221
 
8.2%
r 87138
 
7.2%
n 81095
 
6.7%
v 79419
 
6.6%
78197
 
6.5%
o 55061
 
4.5%
t 52951
 
4.4%
M 41489
 
3.4%
Other values (15) 265189
21.9%

skills_desc
Text

MISSING 

Distinct2212
Distinct (%)90.7%
Missing121410
Missing (%)98.0%
Memory size967.7 KiB
2024-10-17T22:09:05.967358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length3602
Median length33
Mean length204.89873
Min length3

Characters and Unicode

Total characters499748
Distinct characters107
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2131 ?
Unique (%)87.4%

Sample

1st rowRequirements: We are seeking a College or Graduate Student (can also be completed with school) with a focus in Planning, Architecture, Real Estate Development or Management or General Business. Must be able to work in an extremely fast paced environment and able to multitask and prioritize.
2nd rowWe are currently accepting resumes for FOH - Asisstant Restaurant Management with a strong focus on delivering high quality customer service. Prefer 1 to 3 years FOH management experience. Candidate should be a self-starter, proactive, attentive to details and like developing others. Must have a strong sense of teamwork and strong witten and verbal communication skills. Have a keen interest in service, food and learning. Passion for excellence and doing things right.
3rd rowThis position requires a baseline understanding of online marketing including Search Engine Marketing, Search Engine Optimization, and campaign analytics. The ideal candidate must be an analytical and detailed dynamic, self-starter who is proactive, and able to multitask effectively. This individual must be a strategic thinker with excellent verbal and written communication, as well strong presentation skills and the ability to work independently in an organized manner.
4th row• Requires the ability to communicate effective, both verbally and in writing • Requires basic computer skills EDUCATION AND EXPERIENCE: • Graduate of an accredited school of occupational therapy • Must possess current valid Nebraska State License in Occupational Therapy • Must possess current valid registration by the NBCOT ( National Board for Certification in Occupational Therapy) • Must be certified in Basic Life Support • Experience with pediatric patients preferred
5th rowKnowledge, Skills and Abilities: 1. Proficient with computer technology such as Microsoft Office. Also proficiency with (or ability to learn) ProPresenter and online applications such as Google Calendar and Planning center. Understanding and skill in Photoshop, Adobe Premiere a plus. 2. Good writing, analytical and problem‐solving skills. 3. Good knowledge of social networking applications such as Facebook, Twitter. 4. Ability to communicate effectively verbally and in writing. 5. Ability to operate standard office equipment, including but not limited to, computers, telephone systems, copiers/printers and facsimile machines. 6. Ability to follow oral and written instructions. 7. Follow-up skills with great attention to detail. 8. Coachable ability in graphic design and minimal video editing ability a HUGE plus and preferred, but not required. Minimum Qualifications: 1. At least two (2) years of experience in general office responsibilities and procedures and two (2) years of graphics design and media background. 2. Must be proficient in computer usage, both internet and word processing. 3. Knowledge of principles and practices of basic office management and organization. 4. Ability to work well either alone or as part of a team. 5. Must be fully committed to the mission of FBC Melbourne/Bay West Church
ValueCountFrequency (%)
and 2726
 
4.4%
to 1934
 
3.1%
the 1673
 
2.7%
of 1360
 
2.2%
980
 
1.6%
in 893
 
1.4%
a 863
 
1.4%
skills 833
 
1.3%
or 801
 
1.3%
with 692
 
1.1%
Other values (8120) 49190
79.4%
2024-10-17T22:09:06.174175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58869
 
11.8%
e 44344
 
8.9%
i 32847
 
6.6%
n 28263
 
5.7%
t 28210
 
5.6%
a 27770
 
5.6%
o 26611
 
5.3%
r 24345
 
4.9%
s 23009
 
4.6%
l 17988
 
3.6%
Other values (97) 187492
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
58869
 
11.8%
e 44344
 
8.9%
i 32847
 
6.6%
n 28263
 
5.7%
t 28210
 
5.6%
a 27770
 
5.6%
o 26611
 
5.3%
r 24345
 
4.9%
s 23009
 
4.6%
l 17988
 
3.6%
Other values (97) 187492
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
58869
 
11.8%
e 44344
 
8.9%
i 32847
 
6.6%
n 28263
 
5.7%
t 28210
 
5.6%
a 27770
 
5.6%
o 26611
 
5.3%
r 24345
 
4.9%
s 23009
 
4.6%
l 17988
 
3.6%
Other values (97) 187492
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
58869
 
11.8%
e 44344
 
8.9%
i 32847
 
6.6%
n 28263
 
5.7%
t 28210
 
5.6%
a 27770
 
5.6%
o 26611
 
5.3%
r 24345
 
4.9%
s 23009
 
4.6%
l 17988
 
3.6%
Other values (97) 187492
37.5%

listed_time
Real number (ℝ)

HIGH CORRELATION 

Distinct53231
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7132044 × 1012
Minimum1.711317 × 1012
Maximum1.7135728 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:06.241576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.711317 × 1012
5-th percentile1.7123695 × 1012
Q11.7128855 × 1012
median1.7134076 × 1012
Q31.7134836 × 1012
95-th percentile1.7135637 × 1012
Maximum1.7135728 × 1012
Range2.255802 × 109
Interquartile range (IQR)5.98084 × 108

Descriptive statistics

Standard deviation3.989122 × 108
Coefficient of variation (CV)0.00023284565
Kurtosis-0.44643344
Mean1.7132044 × 1012
Median Absolute Deviation (MAD)1.30063 × 108
Skewness-1.0188878
Sum2.1217866 × 1017
Variance1.5913094 × 1017
MonotonicityNot monotonic
2024-10-17T22:09:06.294460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.712374714 × 1012123
 
0.1%
1.713518539 × 101296
 
0.1%
1.713518364 × 101294
 
0.1%
1.713493196 × 101291
 
0.1%
1.713277799 × 101290
 
0.1%
1.713472823 × 101288
 
0.1%
1.712885166 × 101288
 
0.1%
1.71289665 × 101287
 
0.1%
1.713494573 × 101285
 
0.1%
1.712904383 × 101285
 
0.1%
Other values (53221) 122922
99.3%
ValueCountFrequency (%)
1.711317014 × 10121
 
< 0.1%
1.712345672 × 10123
< 0.1%
1.712345673 × 10121
 
< 0.1%
1.712345675 × 10123
< 0.1%
1.712345677 × 10121
 
< 0.1%
1.712345684 × 10121
 
< 0.1%
1.712345685 × 10121
 
< 0.1%
1.712345686 × 10123
< 0.1%
1.712345687 × 10121
 
< 0.1%
1.712345688 × 10121
 
< 0.1%
ValueCountFrequency (%)
1.713572816 × 10123
 
< 0.1%
1.713572803 × 10121
 
< 0.1%
1.71357279 × 10121
 
< 0.1%
1.713572788 × 10121
 
< 0.1%
1.713572785 × 10124
< 0.1%
1.713572776 × 10121
 
< 0.1%
1.713572774 × 10122
 
< 0.1%
1.713572773 × 10128
< 0.1%
1.713572768 × 10121
 
< 0.1%
1.713572767 × 10121
 
< 0.1%

posting_domain
Text

MISSING 

Distinct4443
Distinct (%)5.3%
Missing39968
Missing (%)32.3%
Memory size967.7 KiB
2024-10-17T22:09:06.414062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length60
Median length49
Mean length21.795055
Min length5

Characters and Unicode

Total characters1828191
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1186 ?
Unique (%)1.4%

Sample

1st rowarrowstaffingservices.thejobnetwork.com
2nd rowwilliamsleagroupltd.thejobnetwork.com
3rd rowbartlettroofing.thejobnetwork.com
4th rowtempmee.thejobnetwork.com
5th rowintegritymarketinggroup.thejobnetwork.com
ValueCountFrequency (%)
www.click2apply.net 3811
 
4.5%
click.appcast.io 2255
 
2.7%
jsv3.recruitics.com 1921
 
2.3%
jobs.smartrecruiters.com 1557
 
1.9%
boards.greenhouse.io 1493
 
1.8%
rr.jobsyn.org 1202
 
1.4%
recruiting.ultipro.com 1175
 
1.4%
recruiting.adp.com 809
 
1.0%
sjobs.brassring.com 785
 
0.9%
dsp.prng.co 766
 
0.9%
Other values (4432) 68107
81.2%
2024-10-17T22:09:06.608162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 180006
 
9.8%
o 172709
 
9.4%
c 157125
 
8.6%
e 142617
 
7.8%
r 136981
 
7.5%
a 111051
 
6.1%
s 109341
 
6.0%
m 97331
 
5.3%
i 78157
 
4.3%
t 72702
 
4.0%
Other values (53) 570171
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1828191
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 180006
 
9.8%
o 172709
 
9.4%
c 157125
 
8.6%
e 142617
 
7.8%
r 136981
 
7.5%
a 111051
 
6.1%
s 109341
 
6.0%
m 97331
 
5.3%
i 78157
 
4.3%
t 72702
 
4.0%
Other values (53) 570171
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1828191
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 180006
 
9.8%
o 172709
 
9.4%
c 157125
 
8.6%
e 142617
 
7.8%
r 136981
 
7.5%
a 111051
 
6.1%
s 109341
 
6.0%
m 97331
 
5.3%
i 78157
 
4.3%
t 72702
 
4.0%
Other values (53) 570171
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1828191
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 180006
 
9.8%
o 172709
 
9.4%
c 157125
 
8.6%
e 142617
 
7.8%
r 136981
 
7.5%
a 111051
 
6.1%
s 109341
 
6.0%
m 97331
 
5.3%
i 78157
 
4.3%
t 72702
 
4.0%
Other values (53) 570171
31.2%

sponsored
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size967.7 KiB
0
123849 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters123849
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 123849
100.0%

Length

2024-10-17T22:09:06.672525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:06.705654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 123849
100.0%

Most occurring characters

ValueCountFrequency (%)
0 123849
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 123849
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 123849
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 123849
100.0%

work_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size967.7 KiB
FULL_TIME
98814 
CONTRACT
12117 
PART_TIME
 
9696
TEMPORARY
 
1190
INTERNSHIP
 
983
Other values (2)
 
1049

Length

Max length10
Median length9
Mean length8.8943714
Min length5

Characters and Unicode

Total characters1101559
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFULL_TIME
2nd rowFULL_TIME
3rd rowFULL_TIME
4th rowFULL_TIME
5th rowFULL_TIME

Common Values

ValueCountFrequency (%)
FULL_TIME 98814
79.8%
CONTRACT 12117
 
9.8%
PART_TIME 9696
 
7.8%
TEMPORARY 1190
 
1.0%
INTERNSHIP 983
 
0.8%
VOLUNTEER 562
 
0.5%
OTHER 487
 
0.4%

Length

2024-10-17T22:09:06.743346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:06.784014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
full_time 98814
79.8%
contract 12117
 
9.8%
part_time 9696
 
7.8%
temporary 1190
 
1.0%
internship 983
 
0.8%
volunteer 562
 
0.5%
other 487
 
0.4%

Most occurring characters

ValueCountFrequency (%)
L 198190
18.0%
T 145662
13.2%
E 112294
10.2%
I 110476
10.0%
M 109700
10.0%
_ 108510
9.9%
U 99376
9.0%
F 98814
9.0%
R 26225
 
2.4%
C 24234
 
2.2%
Other values (8) 68078
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1101559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 198190
18.0%
T 145662
13.2%
E 112294
10.2%
I 110476
10.0%
M 109700
10.0%
_ 108510
9.9%
U 99376
9.0%
F 98814
9.0%
R 26225
 
2.4%
C 24234
 
2.2%
Other values (8) 68078
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1101559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 198190
18.0%
T 145662
13.2%
E 112294
10.2%
I 110476
10.0%
M 109700
10.0%
_ 108510
9.9%
U 99376
9.0%
F 98814
9.0%
R 26225
 
2.4%
C 24234
 
2.2%
Other values (8) 68078
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1101559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 198190
18.0%
T 145662
13.2%
E 112294
10.2%
I 110476
10.0%
M 109700
10.0%
_ 108510
9.9%
U 99376
9.0%
F 98814
9.0%
R 26225
 
2.4%
C 24234
 
2.2%
Other values (8) 68078
 
6.2%

currency
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing87776
Missing (%)70.9%
Memory size967.7 KiB
USD
36058 
EUR
 
6
CAD
 
3
BBD
 
2
AUD
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters108219
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 36058
29.1%
EUR 6
 
< 0.1%
CAD 3
 
< 0.1%
BBD 2
 
< 0.1%
AUD 2
 
< 0.1%
GBP 2
 
< 0.1%
(Missing) 87776
70.9%

Length

2024-10-17T22:09:06.830212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:06.871349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
usd 36058
> 99.9%
eur 6
 
< 0.1%
cad 3
 
< 0.1%
bbd 2
 
< 0.1%
aud 2
 
< 0.1%
gbp 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
U 36066
33.3%
D 36065
33.3%
S 36058
33.3%
E 6
 
< 0.1%
R 6
 
< 0.1%
B 6
 
< 0.1%
A 5
 
< 0.1%
C 3
 
< 0.1%
G 2
 
< 0.1%
P 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 36066
33.3%
D 36065
33.3%
S 36058
33.3%
E 6
 
< 0.1%
R 6
 
< 0.1%
B 6
 
< 0.1%
A 5
 
< 0.1%
C 3
 
< 0.1%
G 2
 
< 0.1%
P 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 36066
33.3%
D 36065
33.3%
S 36058
33.3%
E 6
 
< 0.1%
R 6
 
< 0.1%
B 6
 
< 0.1%
A 5
 
< 0.1%
C 3
 
< 0.1%
G 2
 
< 0.1%
P 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 36066
33.3%
D 36065
33.3%
S 36058
33.3%
E 6
 
< 0.1%
R 6
 
< 0.1%
B 6
 
< 0.1%
A 5
 
< 0.1%
C 3
 
< 0.1%
G 2
 
< 0.1%
P 2
 
< 0.1%

compensation_type
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing87776
Missing (%)70.9%
Memory size967.7 KiB
BASE_SALARY
36073 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters396803
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBASE_SALARY
2nd rowBASE_SALARY
3rd rowBASE_SALARY
4th rowBASE_SALARY
5th rowBASE_SALARY

Common Values

ValueCountFrequency (%)
BASE_SALARY 36073
29.1%
(Missing) 87776
70.9%

Length

2024-10-17T22:09:06.915259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-17T22:09:06.951038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
base_salary 36073
100.0%

Most occurring characters

ValueCountFrequency (%)
A 108219
27.3%
S 72146
18.2%
B 36073
 
9.1%
E 36073
 
9.1%
_ 36073
 
9.1%
L 36073
 
9.1%
R 36073
 
9.1%
Y 36073
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 396803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 108219
27.3%
S 72146
18.2%
B 36073
 
9.1%
E 36073
 
9.1%
_ 36073
 
9.1%
L 36073
 
9.1%
R 36073
 
9.1%
Y 36073
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 396803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 108219
27.3%
S 72146
18.2%
B 36073
 
9.1%
E 36073
 
9.1%
_ 36073
 
9.1%
L 36073
 
9.1%
R 36073
 
9.1%
Y 36073
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 396803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 108219
27.3%
S 72146
18.2%
B 36073
 
9.1%
E 36073
 
9.1%
_ 36073
 
9.1%
L 36073
 
9.1%
R 36073
 
9.1%
Y 36073
 
9.1%

normalized_salary
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct7266
Distinct (%)20.1%
Missing87776
Missing (%)70.9%
Infinite0
Infinite (%)0.0%
Mean205327.04
Minimum0
Maximum5.356 × 108
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:06.991791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33280
Q152000
median81500
Q3125000
95-th percentile200000
Maximum5.356 × 108
Range5.356 × 108
Interquartile range (IQR)73000

Descriptive statistics

Standard deviation5097626.8
Coefficient of variation (CV)24.826866
Kurtosis5169.0276
Mean205327.04
Median Absolute Deviation (MAD)33826.35
Skewness65.298969
Sum7.4067622 × 109
Variance2.5985799 × 1013
MonotonicityNot monotonic
2024-10-17T22:09:07.045295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41600 416
 
0.3%
100000 410
 
0.3%
75000 393
 
0.3%
80000 388
 
0.3%
70000 387
 
0.3%
90000 375
 
0.3%
37440 359
 
0.3%
130000 352
 
0.3%
85000 337
 
0.3%
65000 332
 
0.3%
Other values (7256) 32324
 
26.1%
(Missing) 87776
70.9%
ValueCountFrequency (%)
0 14
< 0.1%
1 1
 
< 0.1%
1.5 1
 
< 0.1%
13 1
 
< 0.1%
14.875 1
 
< 0.1%
15 1
 
< 0.1%
15.895 1
 
< 0.1%
16 1
 
< 0.1%
16.34 3
 
< 0.1%
16.5 2
 
< 0.1%
ValueCountFrequency (%)
535600000 1
< 0.1%
362408800 1
< 0.1%
286000000 2
< 0.1%
260000000 1
< 0.1%
230423440 1
< 0.1%
228800000 1
< 0.1%
187200000 1
< 0.1%
156000000 1
< 0.1%
135200000 1
< 0.1%
124800000 1
< 0.1%

zip_code
Real number (ℝ)

MISSING 

Distinct6989
Distinct (%)6.8%
Missing20872
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean50400.492
Minimum1001
Maximum99901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:07.095373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile6101
Q124112
median48059
Q378201
95-th percentile95501
Maximum99901
Range98900
Interquartile range (IQR)54089

Descriptive statistics

Standard deviation30252.233
Coefficient of variation (CV)0.60023685
Kurtosis-1.3426502
Mean50400.492
Median Absolute Deviation (MAD)27278
Skewness0.068622569
Sum5.1900915 × 109
Variance9.1519757 × 108
MonotonicityNot monotonic
2024-10-17T22:09:07.146299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 2765
 
2.2%
60601 1837
 
1.5%
77002 1762
 
1.4%
75201 1399
 
1.1%
30303 1367
 
1.1%
2108 1176
 
0.9%
78701 1083
 
0.9%
28202 1075
 
0.9%
85003 1064
 
0.9%
90001 1059
 
0.9%
Other values (6979) 88390
71.4%
(Missing) 20872
 
16.9%
ValueCountFrequency (%)
1001 5
 
< 0.1%
1002 7
 
< 0.1%
1007 1
 
< 0.1%
1008 3
 
< 0.1%
1013 20
< 0.1%
1027 3
 
< 0.1%
1028 1
 
< 0.1%
1035 5
 
< 0.1%
1038 2
 
< 0.1%
1040 9
< 0.1%
ValueCountFrequency (%)
99901 5
 
< 0.1%
99835 1
 
< 0.1%
99801 12
< 0.1%
99737 2
 
< 0.1%
99705 4
 
< 0.1%
99702 1
 
< 0.1%
99701 25
< 0.1%
99676 1
 
< 0.1%
99669 4
 
< 0.1%
99664 3
 
< 0.1%

fips
Real number (ℝ)

MISSING 

Distinct2270
Distinct (%)2.4%
Missing27415
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean28713.88
Minimum1003
Maximum56045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size967.7 KiB
2024-10-17T22:09:07.194844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile6019
Q113121
median29183
Q342077
95-th percentile51840
Maximum56045
Range55042
Interquartile range (IQR)28956

Descriptive statistics

Standard deviation16015.93
Coefficient of variation (CV)0.55777658
Kurtosis-1.3266053
Mean28713.88
Median Absolute Deviation (MAD)15830
Skewness-0.067056655
Sum2.7689943 × 109
Variance2.5651001 × 108
MonotonicityNot monotonic
2024-10-17T22:09:07.242269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36061 2765
 
2.2%
6037 2396
 
1.9%
17031 2360
 
1.9%
48113 2263
 
1.8%
48201 2146
 
1.7%
4013 1758
 
1.4%
53033 1465
 
1.2%
13121 1460
 
1.2%
6085 1216
 
1.0%
25025 1194
 
1.0%
Other values (2260) 77411
62.5%
(Missing) 27415
 
22.1%
ValueCountFrequency (%)
1003 24
< 0.1%
1005 6
 
< 0.1%
1007 1
 
< 0.1%
1009 1
 
< 0.1%
1011 1
 
< 0.1%
1013 3
 
< 0.1%
1015 23
< 0.1%
1017 6
 
< 0.1%
1021 5
 
< 0.1%
1023 2
 
< 0.1%
ValueCountFrequency (%)
56045 1
 
< 0.1%
56043 8
< 0.1%
56041 3
 
< 0.1%
56039 9
< 0.1%
56037 2
 
< 0.1%
56035 1
 
< 0.1%
56033 7
 
< 0.1%
56031 3
 
< 0.1%
56029 3
 
< 0.1%
56025 19
< 0.1%

Interactions

2024-10-17T22:08:58.843581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.055469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.642563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.244290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.839722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.433126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.991870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.511543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.030202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.611236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.189997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.190649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.753298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.288350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.884597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.111104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.691908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.287526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.884824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.473061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.039476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.551501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.074732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.655539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.230592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.233178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.795024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.330680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.924790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.153600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.732947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.330916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.929297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.501799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.080650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.589642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.119137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.699266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.263580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.275936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.832433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.371598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.963758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.194217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.772260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.371893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.972568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.540555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.117413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.626891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.162174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.742199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.302101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.316765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.870431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.411194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.999949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.233535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.813738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.409072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.010303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.581575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.152942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.661363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.199300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.780705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.338785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.352826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.907681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.448274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.038347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.276190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.852011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.450556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.057880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.619759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.182469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.702362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.241523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.821727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.843489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.394130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.947814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.487207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.074570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.313069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.889830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.485509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.093995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.646983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.215332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.737448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.278268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.858645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.877215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.435488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.983143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.523157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.112245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.354235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.928174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.527871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.139106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.684772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.251274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.773041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.319346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.897845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.905463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.477056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.020529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.560267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.154421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.397156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.000431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.575403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.182191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.728081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.292468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.812123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.361466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.944026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.945587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.520411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.062838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.603640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.194470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.438722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.043561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.616724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.222582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.769274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.330484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.851230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.403556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.984636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.984784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.559579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.101268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.643557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.236548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.481255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.079646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.668061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.264591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.805744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.367273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.879893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.446417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.028810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.022553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.601243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.138008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.686375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.273545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.519035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.121542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.706002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.307796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.843364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.405477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.917839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.486638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.068326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.062235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.638157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.175827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.724385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.313097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.558225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.161257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.748674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.352711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.907259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.441069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.956653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.529295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.110407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.110992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.677302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.211725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.764464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:59.354116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:51.598556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.200559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:52.791199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.395884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:53.946529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.476043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:54.991816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:55.571072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:56.151326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.148978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:57.716916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.249405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-17T22:08:58.803834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-17T22:09:07.285108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
application_typeappliesclosed_timecompany_idcurrencyexpiryfipsformatted_experience_levelformatted_work_typejob_idlisted_timemax_salarymed_salarymin_salarynormalized_salaryoriginal_listed_timepay_periodviewswork_typezip_code
application_type1.0000.0370.4350.1430.0100.1380.0410.1960.1800.0240.0780.0200.0850.0200.0180.0440.0670.0100.1800.041
applies0.0371.000NaN0.1030.000-0.010-0.0060.0110.030-0.035-0.0510.0090.1750.0270.122-0.0300.0060.8210.030-0.012
closed_time0.435NaN1.000-0.2740.0000.810-0.1820.0000.2020.8250.8860.1070.0040.1000.0900.8630.0000.2730.2020.212
company_id0.1430.103-0.2741.0000.0000.020-0.0270.0460.042-0.049-0.031-0.0010.1200.0340.0240.0090.056-0.0130.0420.004
currency0.0100.0000.0000.0001.0000.0070.0180.0000.0000.0000.0160.0001.0000.0000.0000.0000.0000.0000.0000.018
expiry0.138-0.0100.8100.0200.0071.000-0.0110.0250.0270.7810.924-0.012-0.017-0.015-0.0030.8710.043-0.2470.0270.004
fips0.041-0.006-0.182-0.0270.018-0.0111.0000.0360.030-0.003-0.006-0.029-0.037-0.032-0.044-0.0070.053-0.0150.030-0.159
formatted_experience_level0.1960.0110.0000.0460.0000.0250.0361.0000.2600.0000.0341.0000.1831.0000.0000.0120.1800.0150.2600.037
formatted_work_type0.1800.0300.2020.0420.0000.0270.0300.2601.0000.0050.0360.0000.0530.0000.0000.0170.2330.0001.0000.025
job_id0.024-0.0350.825-0.0490.0000.781-0.0030.0000.0051.0000.852-0.002-0.053-0.0050.0090.7980.000-0.1950.005-0.023
listed_time0.078-0.0510.886-0.0310.0160.924-0.0060.0340.0360.8521.000-0.007-0.046-0.0110.0090.9390.039-0.2930.0360.000
max_salary0.0200.0090.107-0.0010.000-0.012-0.0291.0000.000-0.002-0.0071.000NaN0.9650.813-0.0310.0000.0900.000-0.020
med_salary0.0850.1750.0040.1201.000-0.017-0.0370.1830.053-0.053-0.046NaN1.000NaN0.769-0.0340.4320.1180.053-0.037
min_salary0.0200.0270.1000.0340.000-0.015-0.0321.0000.000-0.005-0.0110.965NaN1.0000.798-0.0260.0000.0940.000-0.029
normalized_salary0.0180.1220.0900.0240.000-0.003-0.0440.0000.0000.0090.0090.8130.7690.7981.000-0.0030.0000.1250.0000.035
original_listed_time0.044-0.0300.8630.0090.0000.871-0.0070.0120.0170.7980.939-0.031-0.034-0.026-0.0031.0000.017-0.2680.017-0.005
pay_period0.0670.0060.0000.0560.0000.0430.0530.1800.2330.0000.0390.0000.4320.0000.0000.0171.0000.0000.2330.058
views0.0100.8210.273-0.0130.000-0.247-0.0150.0150.000-0.195-0.2930.0900.1180.0940.125-0.2680.0001.0000.0000.009
work_type0.1800.0300.2020.0420.0000.0270.0300.2601.0000.0050.0360.0000.0530.0000.0000.0170.2330.0001.0000.025
zip_code0.041-0.0120.2120.0040.0180.004-0.1590.0370.025-0.0230.000-0.020-0.037-0.0290.035-0.0050.0580.0090.0251.000

Missing values

2024-10-17T22:08:59.454005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-17T22:08:59.739954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-17T22:09:00.509834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

job_idcompany_nametitledescriptionmax_salarypay_periodlocationcompany_idviewsmed_salarymin_salaryformatted_work_typeappliesoriginal_listed_timeremote_allowedjob_posting_urlapplication_urlapplication_typeexpiryclosed_timeformatted_experience_levelskills_desclisted_timeposting_domainsponsoredwork_typecurrencycompensation_typenormalized_salaryzip_codefips
0921716Corcoran Sawyer SmithMarketing CoordinatorJob descriptionA leading real estate firm in New Jersey is seeking an administrative Marketing Coordinator with some experience in graphic design. You will be working closely with our fun, kind, ambitious members of the sales team and our dynamic executive team on a daily basis. This is an opportunity to be part of a fast-growing, highly respected real estate brokerage with a reputation for exceptional marketing and extraordinary culture of cooperation and inclusion.Who you are:You must be a well-organized, creative, proactive, positive, and most importantly, kind-hearted person. Please, be responsible, respectful, and cool-under-pressure. Please, be proficient in Adobe Creative Cloud (Indesign, Illustrator, Photoshop) and Microsoft Office Suite. Above all, have fantastic taste and be a good-hearted, fun-loving person who loves working with people and is eager to learn.Role:Our office is a fast-paced environment. You’ll work directly with a Marketing team and communicate daily with other core staff and our large team of agents. This description is a brief overview, but your skills and interests will be considered in what you work on and as the role evolves over time.Agent Assistance- Receive & Organize Marketing Requests from Agents- Track Tasks & Communicate with Marketing team & Agents on Status- Prepare print materials and signs for open houses- Submit Orders to Printers & Communicate & Track DeadlinesGraphic Design & Branding- Managing brand strategy and messaging through website, social media, videos, online advertising, print placement and events- Receive, organize, and prioritize marketing requests from agents- Fulfill agent design requests including postcards, signs, email marketing and property brochures using pre-existing templates and creating custom designs- Maintain brand assets and generic filesEvents & Community- Plan and execute events and promotions- Manage Contacts & Vendors for Event Planning & SponsorshipsOur company is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.Job Type: Full-time\nPay: $18-20/hour\nExpected hours: 35 – 45 per week\nBenefits:Paid time offSchedule:8 hour shiftMonday to FridayExperience:Marketing: 1 year (Preferred)Graphic design: 2 years (Preferred)Work Location: In person\n20.0HOURLYPrinceton, NJ2774458.020.0NaN17.0Full-time2.01.713398e+12NaNhttps://www.linkedin.com/jobs/view/921716/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.715990e+12NaNNaNRequirements: \n\nWe are seeking a College or Graduate Student (can also be completed with school) with a focus in Planning, Architecture, Real Estate Development or Management or General Business. Must be able to work in an extremely fast paced environment and able to multitask and prioritize.1.713398e+12NaN0FULL_TIMEUSDBASE_SALARY38480.08540.034021.0
11829192NaNMental Health Therapist/CounselorAt Aspen Therapy and Wellness , we are committed to serving clients with best practices to help them with change, improvements and better quality of life. We believe in providing a secure, supportive environment to grow as a clinician and learn how to foster longevity in the career which is part of our mission statement.\nThank you for taking the time to explore a career with us. We are excited to be a new group practice in the community. If you are looking for quality supervision as you work towards licensure and ability to serve populations while accepting a variety of insurance panels, we may be a good fit. Our supervisors are trained in EMDR and utilize a parts work perspective with a trauma lens.\nWe are actively looking to hire a therapist in the area who is passionate about working with adults and committed to growth and excellence in the field. We are located in Old Town Square, Fort Collins.\nWe value and are strengthened by diversity and desire a warm and welcoming place for all people. We believe in racial and ethnic equality, gender equity and social inclusion.\nPosition Requirement Possibilities:A graduate level psychological counseling-related degreeMasters of Social Work (MSW/LSW)Licensed Professional Counselor Candidate (LPCC)Clinical Social worker (LCSW)Professional Counselor (LPC)Marriage/Family Therapist (LMFT)Relating to this?Wanting to deliver high quality mental healthcareSeeking quality supervision and growth in a healthy environmentWhat we offer:Flexible work scheduleW2 Employment - commission basedBuilding to full time workJump of 5% in commission as well as monthly bonus/stipend once full timeWeekly supervision providedPaid weekly team meetings $30/hrTwo paid wellness hours/month $30/hrTelemedicine and in-person flexibilitySupportive work environment with direct access to two supervisorsAdministrative supportApproved professional development training providedFully automated EHR and technology supportStrong work/life balanceJob Duties:Conducting intake assessmentsDeveloping and implementing treatment plans for clients based on assessment and coordinating any additional services needed, revising as necessaryConducting individual sessions as appropriate for the treatment plan of the patientApplying psychotherapeutic techniques and interventions in the delivery of services to individuals for the purpose of treating emotional and behavioral disorders that have been diagnosed in assessmentParticipating in team meetings in order to staff new cases. Presenting appropriate patient information to the team. Recommending effective treatment interventions.Building and maintaining an active caseload with assigned clientsCompleting timely progress notes and treatment updates in the EHR. Maintaining all clinical documentation in accordance with regulatory and accrediting standardsProviding crisis intervention to patients in acute distress and referring as neededPerforming case management and discharge planning as neededExcellent communication and interpersonal skillsCompassionate and empathetic approach to patient carePlease send resume and cover letter to info@aspentherapyandwellness.com\nAbout Aspen Therapy and Wellness LLCAspen Therapy and Wellness is a mental health services provider focusing on work with adults in an outpatient setting, working with a variety of mental health issues both in-person in Old Town Fort Collins and throughout the state of Colorado via telehealth services.\nPlease note that this job description is not exhaustive and additional duties may be assigned as needed.50.0HOURLYFort Collins, CONaN1.0NaN30.0Full-timeNaN1.712858e+12NaNhttps://www.linkedin.com/jobs/view/1829192/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.715450e+12NaNNaNNaN1.712858e+12NaN0FULL_TIMEUSDBASE_SALARY83200.080521.08069.0
210998357The National ExemplarAssitant Restaurant ManagerThe National Exemplar is accepting applications for an Assistant Restaurant Manager.\nWe offer highly competitive wages, healthcare, paid time off, complimentary dining privileges and bonus opportunities. \nWe are a serious, professional, long-standing neighborhood restaurant with over 41 years of service. If you are looking for a long-term fit with a best in class organization then you should apply now. \nPlease send a resumes to pardom@nationalexemplar.com. o65000.0YEARLYCincinnati, OH64896719.08.0NaN45000.0Full-timeNaN1.713278e+12NaNhttps://www.linkedin.com/jobs/view/10998357/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.715870e+12NaNNaNWe are currently accepting resumes for FOH - Asisstant Restaurant Management with a strong focus on delivering high quality customer service. Prefer 1 to 3 years FOH management experience. Candidate should be a self-starter, proactive, attentive to details and like developing others. Must have a strong sense of teamwork and strong witten and verbal communication skills. Have a keen interest in service, food and learning. Passion for excellence and doing things right.1.713278e+12NaN0FULL_TIMEUSDBASE_SALARY55000.045202.039061.0
323221523Abrams Fensterman, LLPSenior Elder Law / Trusts and Estates Associate AttorneySenior Associate Attorney - Elder Law / Trusts and Estates Our legal team is committed to providing each client with quality counsel, innovative solutions, and personalized service. Founded in 2000, the firm offers the legal expertise of its 115+ attorneys, who have accumulated experience and problem-solving skills over decades of practice.\nWe are a prominent Lake Success Law Firm seeking an associate attorney for its growing Elder Law and Estate Planning practice. The successful candidate will be a self-motivated, detail-oriented team member with strong communication skills and a desire to grow their practice. Experience with Estate Planning, Administration, and Litigation and is preferred.\n Responsibilities will include:\nCounseling clients with regard to estate planning and asset protection;Formulating and overseeing execution of Medicaid and estate plans;Drafting wills, revocable and irrevocable trusts, powers of attorney, health care proxies, and living wills;Estate Administration;Trust Administration;Court Appearances for Estate and Proceedings;Supervising paralegals \nQualifications:Juris Doctor degree (J.D.) from an accredited law schoolLicensed to practice law in New York10-15 years of experienceExperience with various advance directives, trusts, and willsStrong analytical and problem-solving skillsAbility to build rapport with clientsExcellent written and verbal communication skills\n Competitive salary commensurate with experienceSalary: $140,000- $175,000Benefits: 401k, Medical, Dental, Life Insurance, PTO, and more\nThis position is based out of Lake Success, NY175000.0YEARLYNew Hyde Park, NY766262.016.0NaN140000.0Full-timeNaN1.712896e+12NaNhttps://www.linkedin.com/jobs/view/23221523/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.715488e+12NaNNaNThis position requires a baseline understanding of online marketing including Search Engine Marketing, Search Engine Optimization, and campaign analytics. The ideal candidate must be an analytical and detailed dynamic, self-starter who is proactive, and able to multitask effectively. This individual must be a strategic thinker with excellent verbal and written communication, as well strong presentation skills and the ability to work independently in an organized manner.1.712896e+12NaN0FULL_TIMEUSDBASE_SALARY157500.011040.036059.0
435982263NaNService TechnicianLooking for HVAC service tech with experience in commerical and industrial equipment. Minimum 5 yrs. on the job with mechanical license. Winger is a full line union mechanical business with Piping, plumbing, sheet metal and service.80000.0YEARLYBurlington, IANaN3.0NaN60000.0Full-timeNaN1.713452e+12NaNhttps://www.linkedin.com/jobs/view/35982263/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.716044e+12NaNNaNNaN1.713452e+12NaN0FULL_TIMEUSDBASE_SALARY70000.052601.019057.0
591700727Downtown Raleigh AllianceEconomic Development and Planning InternJob summary:The Economic Development & Planning Intern will provide valuable support to the Economic Development & Planning team, with a specific focus on urban planning and transportation initiatives during the upcoming summer semester. This role is ideal for a local graduate or undergraduate student with a keen interest in economic development, city planning, and a passion for contributing to the growth of a vibrant downtown community.\nResponsibilities/Essential Functions:Support the Planning & Transportation Manager and DRA Economic Development &Planning team in major planning and advocacy initiatives, such as the ongoing Downtown Economic Development Strategy.Assist in coordination efforts related to transportation planning and major downtownprojects such Raleigh’s first Bus Rapid Transit line.Contribute to the creation of reports, including the annual State of Downtown andquarterly market reports.Assist in data collection, analysis, and maintenance of downtown data.Support small business and retail recruitment programs.Participate in stakeholder meetings and community engagement efforts.\nQualifications:Currently enrolled in a graduate or undergraduate program with a focus on urbanplanning, economics, business, research, public administration, geography,sustainability, or related field.Strong interest in economic development, city planning, and community revitalization.Excellent analytical and research skills, with a keen eye for detail.Proficiency in Microsoft Office Suite and data analysis tools.Effective communication skills, both written and verbal.\nBenefits:Gain hands-on experience in economic development and city planning.Work closely with a dynamic and experienced team of economic development andplanning professionals.Networking opportunities with local stakeholders and professionals.Compensation for your contributions\nPhysical Requirements:Prolonged periods sitting at a desk and working on a computer.Must be able to lift up to 15 pounds at times.Must be able to access various departments of a given location.\nPosition Environment: This is an in-person role, with the candidate reporting to the Downtown Raleigh Alliance offices at 333 Fayetteville Street, Suite 1150, Raleigh NC. Office space will be provided onsite at DRA and the intern may also be in the field providing support to our Downtown Raleigh community. DRA will provide parking for regular or required on-site work. DRA will also provide the option of transit passes for regular or on-site work. Travel outside of periodic travel to and from Downtown Raleigh and the DRA office will not be required of this position.\nOther duties:Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.\nCompensation:This position is a temporary, part-time position, at approximately 12 to 20 hours a week and limited to a maximum 14-week term, aligned with the summer university calendar. Anticipated pay range is $14 - $20 an hour commensurate with qualifications and prior experience. This position is non-exempt and not eligible for benefits at DRA.\nHow to Apply:Please submit your resume and cover letter highlighting your interest in economic development and planning to marysell@downtownraleigh.org. Applications will be accepted until Monday, May 6th. Interviews will be scheduled on a rolling basis.\nEEO statement: We are an equal employment opportunity employer and do not discriminate against any person because of race, color, creed, religion, national origin, political affiliation, sex, gender identity or expression, sexual orientation, age, disability, genetic information, or other reasons prohibited by law (referred to as "protected status"). This nondiscrimination and opportunity policy extends to employment, use of all company facilities, membership, board service and leadership, volunteerism, participation in any of the organizations programs or services and all employment actions such as promotions, compensation, benefits and termination of employment.20.0HOURLYRaleigh, NC1481176.09.0NaN14.0Internship4.01.713456e+12NaNhttps://www.linkedin.com/jobs/view/91700727/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.716048e+12NaNNaNNaN1.713456e+12NaN0INTERNSHIPUSDBASE_SALARY35360.027601.037183.0
6103254301Raw CerealProducerCompany DescriptionRaw Cereal is a creative design agency specializing in live, interactive, corporate, and installation-based entertainment. Our mission is to push boundaries and create unique and immersive experiences for our clients. We pride ourselves on our end-to-end creative services and cutting-edge use of technology for larger-than-life productions.\nRole DescriptionWe're looking for Directors, Producers, Creatives, AI Programmers, 3D Artists, Senior Motion Graphics Artists, Editors, etc. \nIf you think you have something to add, please reach out. \nJobs@rawcereal.com300000.0YEARLYUnited States81942316.07.0NaN60000.0Contract1.01.712861e+121.0https://www.linkedin.com/jobs/view/103254301/?trk=jobs_biz_prem_srchNaNSimpleOnsiteApply1.715453e+12NaNNaNNaN1.712861e+12NaN0CONTRACTUSDBASE_SALARY180000.0NaNNaN
7112576855NaNBuilding EngineerSummary: Due to the pending retirement of our building engineer, we are seeking a Building Engineer (BE). The BE is a salaried, overtime-exempt professional with direct responsibility for the physical plant of our historic clubhouse. This hands-on position involves light maintenance tasks, operation of building systems, selection and oversight of outside contractors, and administration of building maintenance records. \nFounded in 1852, the Pacific-Union Club is one of the oldest and most exclusive clubs in the world and is known the world over for its excellent facilities and gracious staff. Our 1910 clubhouse is a National Historic Landmark and a California Designated Landmark. The Club provides dining services, a library, athletic facilities, and overnight accommodation. Qualifications:· Professional training certification or a minimum of 5 years of experience in charge of building maintenance are strongly desired, though we will consider candidates with an equivalent combination of education and experience.· High School degree required, though we will consider candidates with equivalent education or experience · Experience and verifiable competence in building systems including HVAC, steam, gas, electrical, plumbing, repair work and/or equivalent training are required.· Excellent communication skills are required, including proficient oral and written communication. English fluency is required and the ability to speak additional languages is highly desired. · Good organizational and time management skills are required. · Computer competency with word processing, spreadsheets, email, and building systems is required. · Experience in emergency response is highly desired. Must be capable of taking a leadership role in emergency response.· Must demonstrate a working knowledge of building codes and regulations pertaining to all basic trades.· CFC and other trade certifications are desired. · Experience supervising maintenance staff or equivalent training in supervision is highly desired. Physical Requirements: · Standing, sitting, walking, and moving about in a normal fashion for extended periods of time including kneeling, crouching, and climbing ladders.· Reaching by extending hand(s) or arm(s) in any direction.· Ability to bend and lift objects, and push or pull items weighing up to 50 pounds.· Finger dexterity to manipulate objects with fingers rather than with whole hand(s) or arm(s).· Communication skills using the spoken word.· Ability to see and hear within normal parameters. Schedule: The BE’s work schedule shall be determined in accordance with business demands. Typically, the schedule consists of weekday daytime shifts, but the Chief Engineer must be available and prepared to work all shifts and days of the week as needed to accomplish the full range of responsibilities. Responsibilities:· Building maintenance maintains all aspects of the building and grounds to the highest standards of safety, cleanliness, orderliness, efficiency, record keeping and professionalism. The BE is proactive in his duties and makes recommendations for improving systems and procedures.· Project management: coordinates and manages maintenance projects and property renovations.· Supervision: trains and supervises repair/maintenance staff, vendors, and outside contractors. · Serves as the Club’s expert on all building operation systems including HVAC, steam, electrical, plumbing, fire sprinklers, utilities, laundry, fire suppression systems, dish machine, elevator, disabled lift, lighting, irrigation, security, etc.· Emergency response: handles emergencies in cooperation with other management staff and takes an active role in training staff in the safe use of equipment and systems. · Communication: interfaces positively with co-workers, members, and vendors and reports concerns.· Other: additional duties and responsibilities may be assigned from time to time. · Maintains a self-improvement program and keeps abreast of new equipment technology, automation, standards, codes, maintenance procedures, and emergency response.\nBenefits:§ 401(k) retirement plan with 3% employer contribution and up to 5% additional contribution annually for qualified participants; no matching requirement§ Health Plan (choice of 3 plans) with up to 100% individual premiums paid, 75% of dependent premiums paid§ Dental Plan with up to 100% individual premiums paid, 75% of dependent premiums paid§ Life Insurance§ Long Term Disability Insurance§ Paid vacations (accrual begins after 2 mo. of employment)§ 9 paid holidays (after 30 days of employment)§ Break beverages and meals provided§ Work uniform (shirt and pants) provided and maintained by the Club.\nAPPLICATION: Interested applicants should submit a full resume and formal cover letter that explains your interest and qualification for the position. While traditional mail is acceptable, the employer prefers that both documents be sent by email, preferably in PDF format to jobs@puclub.org Traditional mail: Attn: Tom Gaston, The Pacific-Union Club, 1000 California Street, San Francisco, CA 94108 The Club will consider all qualified applicants in accordance with its Equal Employment Opportunity policies and the San Francisco Fair Chance Ordinance.120000.0YEARLYSan Francisco, CANaN2.0NaN90000.0Full-timeNaN1.712443e+12NaNhttps://www.linkedin.com/jobs/view/112576855/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.727995e+12NaNNaNNaN1.712443e+12NaN0FULL_TIMEUSDBASE_SALARY105000.094101.06075.0
81218575Children's NebraskaRespiratory TherapistAt Children’s, the region’s only full-service pediatric healthcare center, our people make us the very best for kids. Come cultivate your passion, purpose and professional development in an environment of excellence and inclusion, where team members are supported and deeply valued. Opportunities for career growth abound as we grow our services and spaces, including the cutting-edge Hubbard Center for Children. Join our highly engaged, caring team—and join us in providing brighter, healthier tomorrows for the children we serve. Children's is committed to diversity and inclusion. We are an equal opportunity employer including veterans and people with disabilities.\nA Brief OverviewProvides appropriate respiratory care specific to the pediatric population in accordance with the hospital policy/procedure. Assesses, plans and implements appropriate respiratory plan of care based on the cardiopulmonary needs of the patients. Evaluates effectiveness of plan of care and recommends revisions to the multidisciplinary care team\nEssential Functions• Set up and operate devices such as mechanical ventilators, therapeutic gas administration apparatus, environmental control systems, and aerosol generators, following specified parameters of treatment. • Determine requirements for treatment, such as type, methods and duration of therapy, precautions to be taken, and medication and dosages, compatible with physician’s orders. • Read physicians’ orders, measure arterial blood gases, and review patient information to assess patient’s condition. • Explain treatment procedures to patients to gain cooperation and allay fears. • Monitor patient’s physiological responses to therapy such as vital signs, arterial blood gases and blood chemistry changes and consult with physician if adverse reactions occur. • Administer therapeutic gases including nitrogen, nitric oxide, heliox, etc. • Enforce safety rules and ensure careful adherence to physicians’ orders. • Maintain charts that contain patient pertinent identification and therapy information. • Inspect, clean, test, and maintain respiratory therapy equipment to ensure equipment is functioning safely and efficiently and notify manager/supervisor when repairs are necessary. • Educate patients and/or their families about the patient’s condition and teach appropriate disease management techniques such as breathing exercises and the use of medications and respiratory equipment. • Perform broncho-pulmonary drainage and assist or instruct patients in performance of breathing exercises. • Conduct lung capacity tests to evaluate patient’s cardiopulmonary functions. • Provide emergency care, including artificial respiration, external cardiac massage and assistance with cardiopulmonary resuscitation. • Complete all required respiratory therapy competency tests within specified timeline. • Demonstrate competency in identified technical skills for the respiratory department at the specific work area.Other duties may also include Clinical Instructor (schedule dependent on the contract with Respiratory Therapy schools): • Supervise contracted Respiratory Therapy (RT) students from specified schools in the clinical hospital setting. • Orients RT students to their role in the hospital, which includes scope of service, policies and procedures, patient safety, and professionalism. • Orients RT students to the equipment used by RT at Children’s. • Provides opportunities for directly supervised hands on learning in the clinical setting. • Responsible for accurate completion and documentation of all Respiratory Therapy performed with RT students. • Responsible for accurate “Hand off” of patient information and ordered Respiratory Therapy performed with RT students.Regular attendance at work is an essential function of the job.Perform physical requirements as described in the Physical Requirements section\nEducation QualificationsGraduate of an accredited AMA approved school of respiratory care accredited by the National Board of Respiratory Care Required andBachelor's Degree From an AMA approved accredited school in respiratory care PreferredExperience QualificationsMinimum 1 year experience in respiratory therapy Preferred andExperience working with pediatric patients PreferredSkills and AbilitiesDemonstrates competency in technical skills related to the Respiratory Therapy department.Licenses and CertificationsRCP - Licensed Respiratory Care Practitioner Current and valid Nebraska license as a Respiratory Care Practitioner Required andBCLS - Basic Life Support through the American Heart Association Required andRRT - Registered Respiratory Therapist Current and valid National Registered Respiratory Therapist (RRT) credential within 1 Year Required andCurrent and valid National Registered Neonatal/Pediatric Respiratory Therapist (RRT-NPS) credential within three years of hire. Required andPALS - Pediatric Advanced Life Support within 180 Days Required\nChildren’s is the very best for kids and the very best for your career! At Children’s, we put YOU first so together, we can improve the life of every child!NaNNaNOmaha, NE721189.03.0NaNNaNFull-timeNaN1.712348e+12NaNhttps://www.linkedin.com/jobs/view/1218575/?trk=jobs_biz_prem_srchwww.childrensnebraska.orgOffsiteApply1.714940e+12NaNNaN• Requires the ability to communicate effective, both verbally and in writing • Requires basic computer skills \n\nEDUCATION AND EXPERIENCE: \n\n• Graduate of an accredited school of occupational therapy • Must possess current valid Nebraska State License in Occupational Therapy • Must possess current valid registration by the NBCOT ( National Board for Certification in Occupational Therapy) • Must be certified in Basic Life Support • Experience with pediatric patients preferred1.712348e+12NaN0FULL_TIMENaNNaNNaN68102.031055.0
92264355Bay West ChurchWorship LeaderIt is an exciting time to be a part of our church! We are looking for the right energetic leader to join the mission to make disciples for Jesus in Palm Bay, Florida and beyond.\nWhat type of candidate are we looking for:This best fit for the position will lead our worship team to creatively craft meaningful, inspiring musical worship in our worship experiences. He or she will also shepherd our team, and help disciple them to make disciples.The ideal candidate will invest their lives as a part of our church family. This position is also for someone who is willing to own this ministry and work to see it move forward.\nSkills to have:- A vibrant, growing relationship with Jesus Christ- Faithful commitment to the vision, mission and leadership standards of our church- Ability to lead our worship musicians to create excellent, authentic modern worship (example: Hillsong, Elevation, etc...)- Exceptional vocal and/or instrumental talent- Multitracks, Planning Center Online, ProPresenter are required or must learn quickly.\nResponsibilities:- Create musical worship for Sunday morning worship gathering- Conduct rehearsals to prepare for Sundays- Other duties as assigned**** Local connections for team building are a plus**This position is part-time to start with the ability to increase as the demands of the church require it.\nHours per week:Less than 10\nWhen you apply, include a link to you leading worship in a worship service setting.NaNMONTHLYPalm Bay, FL28631247.05.0350.0NaNPart-timeNaN1.712456e+12NaNhttps://www.linkedin.com/jobs/view/2264355/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.715048e+12NaNNaNKnowledge, Skills and Abilities: 1. Proficient with computer technology such as Microsoft Office. Also proficiency with (or ability to learn) ProPresenter and online applications such as Google Calendar and Planning center. Understanding and skill in Photoshop, Adobe Premiere a plus.\n 2. Good writing, analytical and problem‐solving skills.\n 3. Good knowledge of social networking applications such as Facebook, Twitter.\n 4. Ability to communicate effectively verbally and in writing.\n 5. Ability to operate standard office equipment, including but not limited to, computers, telephone systems, copiers/printers and facsimile machines.\n 6. Ability to follow oral and written instructions.\n 7. Follow-up skills with great attention to detail.\n 8. Coachable ability in graphic design and minimal video editing ability a HUGE plus and preferred, but not required. \nMinimum Qualifications: 1. At least two (2) years of experience in general office responsibilities and procedures and two (2) years of graphics design and media background.\n 2. Must be proficient in computer usage, both internet and word processing.\n 3. Knowledge of principles and practices of basic office management and organization.\n 4. Ability to work well either alone or as part of a team.\n 5. Must be fully committed to the mission of FBC Melbourne/Bay West Church1.712456e+12NaN0PART_TIMEUSDBASE_SALARY4200.032905.012009.0
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1238393906266165Athena RecruitingCatering Event ManagerThis role handles all the onsite catering and event planning for events. \nThere are typically 45 events a year. 15-18 of them are dinners that have been auctioned off at charity events and the others are full scale events such as charity functions. Smaller events might include wine dinners for 10 guests. There may be 2-3 weddings per year.\nResponsibilities:planningVisual planning and orderingand securing all rental equipment neededand organize all set up and tear down needsevents calendarlocation do’s and don’t (s)perform all walkthroughs and onsite meetings with client and vendors and all cleaning crewswork with full time chef and banquet captain, servers, bartender, other staff\nMost weeks will be 30-35 hours but it may be inconsistent. Example: during early spring there are very few events because of spring break or other community wide events. \nComprehensive training will be provided\nTear down from weekend events will always be on Monday. The site will be off limits for tear down on weekends as the owners use the site. Other needs can be met Tuesdays through Thursdays such as walkthroughs.65000.0YEARLYGreater Indianapolis3056329.03.0NaN50000.0Part-timeNaN1.713571e+12NaNhttps://www.linkedin.com/jobs/view/3906266165/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.716163e+12NaNAssociateNaN1.713571e+12NaN0PART_TIMEUSDBASE_SALARY57500.0NaNNaN
1238403906266212Synectics Inc.Phlebotomist - FloatJob Description\n\nThe Patient Services Representative - Float (PSR - Float) represents the face of our company to patients, both as part of their health routine or for insights into life-defining health decisions.The PSR Float draws quality blood samples from patients and prepares those specimens for lab testing while following established practices and procedures.The PSR Float has direct contact with patients and creates an atmosphere of trust and confidence while explaining procedures to patients and drawing blood specimens in a skillful, safe and accurate manner.Work multiple locations and cover at Patient Service Center/In-Office Phlebotomy locations with minimal notice.Floater covers entire territory in Montgomery County including patient service centers and In office phlebotomy for clients possible weekends.\n\nQualifications\n\nHigh school diploma or equivalent.Medical training: medical assistant or paramedic training preferred.Phlebotomy certification preferred. Required in California, Nevada, and Washington.Ability to provide quality, error free work in a fast-paced environment.Ability to work independently with minimal on-site supervision.Excellent phlebotomy skills to include pediatric and geriatric.Committed to all policies and procedures including Company dress code, Employee Health and Safety, and Everyday Excellence Guiding Principles.Must be able to make decisions based on established procedures and exercise good judgment.Capable of handling multiple priorities in a high volume setting.Must demonstrate superior customer focus; ability to communicate openly and transparently with peers, supervisors and patients; ability to accelerate and embrace change; and knowledge of our business.Three years phlebotomy experience required, inclusive of pediatric, geriatric and capillary collections.Minimum 2 years in a Patient Service Center environment preferred.Customer service in a retail or service environment preferred.Keyboard/data entry experience.\n\nBenefits\n\nHealthcare Insurance: Synectics offers eligible employees and their dependents healthcare coverage through BlueCross BlueShield of Illinois. Eligibility begins on the 1st day of the calendar month following 60 days of continuous full time employment with Synectics. Premiums are subsidized by Synectics.\n\nDental Insurance: Synectics offers eligible employees and their dependents a dental plan through MetLife. Eligibility begins on the 1st day of the calendar month following 60 days of continuous full time employment with Synectics.\n\nVision Insurance: Synectics offers eligible employees vision insurance through VSP. Eligibility begins on the 1st day of the calendar month following 60 days of continuous full time employment with Synectics.401(k) Plan: The Synectics Inc. Investment Savings Retirement Plan. Synectics offers all employees who are 21 years of age or older the opportunity to invest in the 401(k) Plan on the first enrollment date that is at least 30 days after employment begins. Enrollment dates are each January 1st, April 1st, July 1st, and October 1st.\n\nTechnical Certification Bonus: Synectics is pleased to award its employees a bonus of up to $500 for an approved professional certification. In determining the bonus amount, Synectics will consider the cost of the test(s) for any certification relating to your current position, achieved during your employment with us. Only one Certification Bonus per calendar year may be awarded per employee. Only current, active employees will be eligible to receive this bonus. It will be awarded 90 days after the Synectics office has received documentation confirming the successful completion of the certification.\n\nSynectics is an equal opportunity employer.NaNNaNCarroll County, MD413796.04.0NaNNaNContractNaN1.713571e+12NaNhttps://www.linkedin.com/jobs/view/3906266212/?trk=jobs_biz_prem_srchhttps://www.synectics.com/candidate-apply.php/PHLEBOTOMIST+-+FLOAT-TAKOMA+PARK+-MD-US?id=SYNECEXT-1235&source=LinkedInOffsiteApply1.716164e+12NaNEntry levelNaN1.713572e+12www.synectics.com0CONTRACTNaNNaNNaNNaNNaN
1238413906266217The DyrtSenior Frontend/App DeveloperThe Dyrt is the largest digital camping platform in the world, and the number-1 ranked camping app on both iOS and Android. Every second, a new user visits The Dyrt to access our community-driven campground information. With more than 1 million user-submitted campgrounds, reviews, and tips — more than anyone else on the Internet — The Dyrt makes it easier to find campgrounds for the 80+ million people who camp across the United States.\nIf you love the outdoors and want to be part of a fast-growing consumer app, you’re in the right place.\nThe RoleAs a senior engineer at The Dyrt, you will be tasked with building a robust, maintainable app experience for millions of campers who use The Dyrt every year.\nOur current mobile frontend is built mostly with React Native. Work varies from building highly interactive tools, like our search page and Route Planner, to focusing on dynamically generated pages based on backend data and page performance on our campground listings. In this position we will be looking to you for experience, knowledge, and the confidence to make the case for what each situation merits in order to provide an ideal experience for our users.\nOur solution also includes a next.js web front end, and a ruby/rails back-end. Experience in these areas would be a major advantage.\nYou’ll be working on a team with 2-3 other frontend developers and collaborating closely with our Backend, Product, CTO, and Design teams.\n\nThis position will report to the Head of Engineering.We’re looking for people who:Have at least 4 years professional experience developing web or mobile apps with React NativeHaving 4+ years professional experience with modern component-based frameworks like Next.js, React, or Vue are a bonusAre great communicators — Effective communication is key to how we work. We value patience and empathy in our product planning, support, and day-to-day relations. Ability to communicate effectively with other web developers, engineering, and others (marketing, ux/design, product, other engineering teams, etc.) is a critical skill at The DyrtWork well both collaboratively and independently — We come together to pair on tricky problems and architecture, then dive deep on individual tasksAre ready to learn and share knowledge — Everyone comes to our company with their own set of skills and experiences. Cross-training, code review, mentorship, and curiosity all help us build better productsPlay to win — We want to bring on team members who have a winning attitude and a willingness to think outside the box to get things doneCan thrive in a dynamic startup environment\nKey Responsibilities:Own requirement analysis completely along with the teamDefine the longer-term technical vision for scaling and maintaining our mobile frontend codebaseEvaluate tooling and automation, recommend uplifts as necessary to maintain roadmap and quality goalsLead execution on new features and improvement projectsTranslate comps and wireframes into architecture and component execution and delivery plansEstimate complexity and divide up tasks when new projects are brought to the teamWork closely with support on prioritizing field issues and providing technical guidanceDrive pragmatic testing efforts across the mobile codebase using automation tools\nExperience and Requirements:Have at least 4 years professional experience developing web or mobile apps with JavaScript and modern component-based frameworks like Next.js, React, React Native, or VueReact Native: Experience and an understanding of developing with and debugging React Native and the Node Package ManagementAndroid app development: Some proficiency with Android App development and tools. Experience with the Google release process and setup of the Android Development EnvironmentiOS app development: Some proficiency with iOS App development and tools. Experience with the Apple release process and setting up the iOS Development Environment which is a lot more involved than Android. Proficiency in Certificate management and renewalsGit: Some mid-level skills such as rebasing, resolving merge conflicts, and reverting merges. A firm understanding of Github including gitflow and trunk-based development practicesBitrise: Familiarity with automated build systems such as Bitrise (or Jenkins)CSS: Experience doing UI work with CSSTake pride in writing easy to understand, maintainable codeMaintain and enhance existing code quality, organization, and automationHave strong writing and communication skills for documenting technical requirements and coordinating with other teamsProvide constructive feedback on pull requests and are enthusiastic about mentoring junior developers on the teamProactively research best practices for modern JavaScript and for the frameworks and tools used on the jobIdentify and resolve performance concerns and look for opportunities to reduce technical debtWeigh multiple solutions for a problem against business needs and time constraints in order to meet company goalsExperience with agile tools such as JiraExperience with collaborative design tools such as Figma or SketchExperience with the JSON:API specification is a plusExperience with keeping tabs on external changes that impact the project such as SDK EOLs and changes to key requirements for publication in various app stores.\nExtras:Swift and Objective-C: For occasional work in iOS native codeKotlin and Java: For occasional work in Android native codeFirebase: Experience using Google tools such as Remote Config and A/B tooling would help.Sentry or Crashlytics: Experience debugging using external analytic toolsExperience with Bitrise/Jenkins/CodefreshBackend development experience with Ruby on RailsExperience with offline mobile functionalityRelational database skills — we use PostgreSQL views, triggers, and functionsExperience with elasticsearchExperience with certificate renewal and publications (Developer, web, API)Familiarity with MVC, API & data mocking, and ORMCan do performance tuning of app and database codeAble to document details of functionality, design and architecture of a component/featureAbility to lead inspections on test documentation and all other project documentationAbility to accurately estimate feature complete work including documentation and testingAbility to validate timescales defined by a product ownerStrives to automate all manual effort in the project life cycle\nWorking Here:The Dyrt is built by campers, for campers—whether you’re new to camping or have been camping your whole life. We pride ourselves on being a team that is down to earth, can get things done and then some, and strives to be the best.\nWe encourage everyone to spend more time outside, including employees. We offer competitive market-rate salaries, a generous vacation plan, and we even pay employee bonuses for using The Dyrt in the wild.\nThis is a full-time remote position. Employees are expected to have high-speed internet and a professional working environment sufficient for clear video conferencing during regular working hours. Many of our employees work virtually from Portland, OR but we’re flexible on location as long as you’re between Pacific and Eastern time zones. Our founders even work from their van.\nThe Dyrt is an equal opportunity workplace. We believe that the outdoors are for everyone, and are committed to building an inclusive platform and community that encourages, supports, and celebrates all people interested in camping.\nThe Dyrt was started in Portland, OR, is venture-backed, and has 27 employees working virtually around the U.S.\nInterested candidates should submit a cover letter and resume.NaNNaNUnited States6404239.01.0NaNNaNFull-timeNaN1.713571e+121.0https://www.linkedin.com/jobs/view/3906266217/?trk=jobs_biz_prem_srchhttps://the-dyrt.breezy.hr/p/31c6745b3473-senior-frontend-app-developer-react-native-remote?source=linkedinOffsiteApply1.716163e+12NaNMid-Senior levelNaN1.713571e+12NaN0FULL_TIMENaNNaNNaNNaNNaN
1238423906266248GoodRxAccount Manager, Client SuccessGoodRx is America’s healthcare marketplace. Each month, millions of people visit goodrx.com to find reliable health information and discounts for their healthcare — and we’ve helped people save $60 billion since 2011. We provide prescription discounts that are accepted at more than 70,000 pharmacies in the U.S., as well as telehealth services including doctor visits and lab tests. Our services have been positively reviewed by Good Morning America, The New York Times, NBC News, AARP, and many others.\n\nOur goal is to help Americans find convenient and affordable healthcare. We offer solutions for consumers, employers, health plans, and anyone else who shares our desire to provide affordable prescriptions to all Americans.\n\nAbout The Role\n\nAs a member of the Client Success team, you will be responsible for managing and nurturing relationships with our Pharmaceutical and Agency clients, ensuring customer satisfaction, driving revenue growth, and delivering successful campaigns.\n\nIf you are a driven, client-focused individual with a passion for digital marketing, the pharma industry, and a proven ability to deliver successful campaigns while driving revenue growth and maintaining long-term relationships, we encourage you to apply for this exciting opportunity.\n\nResponsibilities\n\nResponsible for the day-to-day client relationship with Pharmaceutical manufacturer customers, while providing excellent customer service and managing all aspects of the client relationshipAnalyze and deliver monthly program metrics to reinforce program ROI and to provide insights to help clients achieve business goalsPartner with Sales to support new business development and organic growth of accounts, upselling and optimizing where applicableCollaborate with internal subject matter experts such as Business Intelligence, Advertising Operations, Product Management, and Marketing to deliver tailored solutions to customersManage implementation process by ensuring stakeholders deliver what is needed on time and within client expectationsWork with clients and agency partners to manage the Regulatory review submission processCreate visual examples of programs using our enterprise CMS toolStay up-to-date with industry trends, digital marketing best practices, and regulatory requirements in the Pharma space\n\nSkills & Qualifications\n\n5+ years of professional experience in Pharmaceutical client and account managementStrong written/verbal communication, excellent relationship management skills, and sharp attention to detailFamiliarity with pharmaceutical regulatory review (MLR / PRC)Ability to apply analytics and data to a value propositionWillingness to think critically and solve problems in new or creative waysA deep curiosity and interest in healthcare technologiesFamiliarity with CRM software like Salesforce and project management platforms like JIRA or Asana\n\nAt GoodRx, pay ranges are determined based on work locations and may vary based on where the successful candidate is hired. The pay ranges below are shown as a guideline, and the successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, and other relevant business and organizational factors. These pay zones may be modified in the future. Please contact your recruiter for additional information.\n\nSan Francisco And Seattle Offices\n\n$96,000.00 - $154,000.00\n\nNew York Office\n\n$88,000.00 - $141,000.00\n\nSanta Monica Office\n\n$80,000.00 - $128,000.00\n\nOther Office Locations:\n\n$72,000.00 - $115,000.00\n\nGoodRx also offers additional compensation programs such as annual cash bonuses and annual equity grants for most positions as well as generous benefits. Our great benefits offerings include medical, dental, and vision insurance, 401(k) with a company match, an ESPP, unlimited vacation, 13 paid holidays, and 72 hours of sick leave. GoodRx also offers additional benefits like mental wellness and financial wellness programs, fertility benefits, generous parental leave, pet insurance, supplemental life insurance for you and your dependents, company-paid short-term and long-term disability, and more!\n\nWe’re committed to growing and empowering a more inclusive community within our company and industry. That’s why we hire and cultivate diverse teams of the best and brightest from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has a seat at the table and the tools, resources, and opportunities to excel.\n\nWith that said, research shows that women and other underrepresented groups apply only if they meet 100% of the criteria. GoodRx is committed to leveling the playing field, and we encourage women, people of color, those in the LGBTQ+ communities, and Veterans to apply for positions even if they don’t necessarily check every box outlined in the job description. Please still get in touch - we’d love to connect and see if you could be good for the role!\n\nGoodRx is America's healthcare marketplace. The company offers the most comprehensive and accurate resource for affordable prescription medications in the U.S., gathering pricing information from thousands of pharmacies coast to coast, as well as a telehealth marketplace for online doctor visits and lab tests. Since 2011, Americans with and without health insurance have saved $60 billion using GoodRx and million consumers visit goodrx.com each month to find discounts and information related to their healthcare. GoodRx is the #1 most downloaded medical app on the iOS and Android app stores. For more information, visit www.goodrx.com.NaNNaNUnited States2466850.022.0NaNNaNFull-timeNaN1.713572e+121.0https://www.linkedin.com/jobs/view/3906266248/?trk=jobs_biz_prem_srchhttps://goodrx.wd1.myworkdayjobs.com/Careers/job/Remote-USA/Account-Manager--Client-Success_JR100227?source=LinkedInOffsiteApply1.716165e+12NaNMid-Senior levelNaN1.713573e+12goodrx.wd1.myworkdayjobs.com0FULL_TIMENaNNaNNaNNaNNaN
1238433906266272TalentBurst, an Inc 5000 companyQuality EngineerPosition: Quality Engineer I (Complaint Investigations), Req#: 6086-1Position: Quality Engineer II (Complaint Investigations), Req#: 6085-1Location: Irvine, CA (100% onsite)Duration: 3 Months Contract\nJob Description:\nEducation and Experience:Bachelor's degree in engineering, 0 - 4 years of experience related work experience requiredProficient with the Microsoft Office suiteExperience with medical device complaint investigations (CAPA's, NCR's, and Root-Cause Analysis)Functional, physical, and visual testing of medical device equipment in a laboratory settingExperience in medical devices (cardiovascular, endovascular, or other critical care/surgical products), healthcare, or a related environment preferred.Experience in complaint investigations, root cause analysis, and the associated risk assessments preferred.Preference is for candidates to have some complaint investigation experience/root cause analysis. Another preference is for medical device experience.\nRoles and Responsibilities:Investigate complex manufacturing product quality and compliance issues reported from the field, analyze results, determine root cause/probable cause, and initiate and review reports.Perform hands-on device investigation using visual, dimensional, and test equipment to determine the root cause.Assess reported events against established risk documentation for clinical and compliance risk(s) and escalate to Quality Management and Product Safety as needed.Participate in escalation tasks and activities, including Project Risk Assessments (PRA) and Corrective/Preventive action(s) – CAPAs, SCARs, as determined by the investigation.Identify and report key complaint metrics per device category and collaborate with applicable manufacturing engineering teams to resolve production/device-related issues.Other incidental duties assigned by Leadership (May assist in Adhoc complaint analysis or metric review).50.0HOURLYIrvine, CA122451.01.0NaN30.0ContractNaN1.713572e+12NaNhttps://www.linkedin.com/jobs/view/3906266272/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.716164e+12NaNMid-Senior levelNaN1.713572e+12NaN0CONTRACTUSDBASE_SALARY83200.092602.0NaN
1238443906267117Lozano SmithTitle IX/Investigations AttorneyOur Walnut Creek office is currently seeking an attorney to join our Title IX and Investigations practice. Our labor and employment team provides counsel to hundreds of public school districts and other public agencies throughout California. The expertise covers the full spectrum of labor and employment law including hiring employees and drafting employment contracts, to collective bargaining, contract grievances and matters of discrimination, retaliation, and misconduct, to layoffs, discipline, and dismissals.\nWe are seeking an attorney with a strong desire to learn and a passion to work with public agencies. We are seeking candidates with five (5) to seven (7) years practicing as an attorney, and a strong passion for working on Title IX/Investigations matters. Administrative hearing experience a plus.\nExperience A Culture Unlike Any OtherWe invite you to check out Lozano Smith, California’s premier public agency law firm. As a law firm, we have a rather unique culture. Simply put, we enjoy each other's company. We like to have fun together, in the office and in the field. We enjoy our work, and equally important, who we work for. We are committed to giving all employees the opportunity to experience meaningful, impactful work and the support needed to grow. Focus On The SpecificsNo Jerks RuleMentoring ProgramWellness ProgramProfessional Development OpportunitiesDiversity, Equity and Inclusion InitiativesChild-Friendly Business Award WinnerBlue Hat Project: Community Engagement ProgramCompetitive Salary, Benefits and Bonus Programs Foundations Of Lozano Smith Lozano Smith’s core is based on relationships, and that has allowed us to remain California’s premier public agency law firm representing hundreds of school districts and municipalities. We are a team of more than 180 passionate and diverse attorneys, paralegals and support staff. Everyone who calls Lozano Smith home understands the importance of being there for each other and our clients when they need us most.195000.0YEARLYWalnut Creek, CA56120.01.0NaN120000.0Full-timeNaN1.713571e+12NaNhttps://www.linkedin.com/jobs/view/3906267117/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.716163e+12NaNMid-Senior levelNaN1.713571e+12NaN0FULL_TIMEUSDBASE_SALARY157500.094595.06013.0
1238453906267126PinterestStaff Software Engineer, ML Serving PlatformAbout Pinterest:\n\nMillions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.\n\nCreating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.\n\nOur new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. \n\nThe ML Platform team provides foundational tools and infrastructure used by hundreds of ML engineers across Pinterest, including recommendations, ads, visual search, growth/notifications, trust and safety. We aim to ensure that ML systems are healthy (production-grade quality) and fast (for modelers to iterate upon).\n\nWe are seeking a highly skilled and experienced Staff Software Engineer to join our ML Serving team and lead the technical strategy. The ML Serving team builds large scale online systems and tools for model inference, deployment, monitoring and feature fetching/logging. ML workloads are increasingly large, complex, interdependent and the efficient use of ML accelerators is critical to our success. We work on various efforts related to adoption, efficiency, performance, algorithms, UX and core infrastructure to enable the scheduling of ML workloads.\n\nYou’ll be part of the ML Platform team in Data Engineering, which aims to ensure healthy and fast ML in all of the 40+ ML use cases across Pinterest.\n\nWhat You’ll Do:\n\nDesign and build large-scale, reliable and efficient ML serving systems for model inference, deployment monitoring and feature logging.Improve the productivity and iteration speed of ML engineers and data scientists.Projects may include: high-performance inference engine with GPUs and hardware accelerators; ML monitoring and observability solutions.Work extensively with ML engineers across Pinterest to understand their requirements, pain points, and build generalized solutions. Also work with partner teams to drive projects requiring cross-team coordination. Provide technical guidance and coaching to more junior engineers in the team.\n\n\nWhat We’re Looking For:\n\nHands-on experience building large-scale ML use cases and systems in production, preferably expertise in SoTA ML inference technologies and optimizations.Strong understanding of ML systems especially around scalability and efficiency.Flexibility to work across different areas: online systems, model optimization, infrastructure optimization, data processing pipelines, etc.Fluency in Python and C++, familiarity with at least one common ML framework.Experience with GPU programming, containerization, orchestration technologies is a plus.\n\n\nRelocation Statement: \n\n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n\n\nAt Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.\n\nInformation regarding the culture at Pinterest and benefits available for this position can be found here.\n\nUS based applicants only\n\n$148,049—$304,496 USD\n\nOur Commitment To Diversity:\n\nPinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify accessibility@pinterest.com for support.\n\nOur Commitment To Diversity:\n\nPinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify accessibility@pinterest.com for support.\n\nNaNNaNUnited States1124131.03.0NaNNaNFull-timeNaN1.713572e+121.0https://www.linkedin.com/jobs/view/3906267126/?trk=jobs_biz_prem_srchhttps://www.pinterestcareers.com/en/jobs/5882432/staff-software-engineer-ml-serving-platform/?source=linkedin_limited_listing&utm_source=linkedin_limited_listingOffsiteApply1.716164e+12NaNMid-Senior levelNaN1.713572e+12www.pinterestcareers.com0FULL_TIMENaNNaNNaNNaNNaN
1238463906267131EPS LearningAccount Executive, Oregon/WashingtonCompany Overview\n\nEPS Learning is a leading K–12 supplemental literacy and math curriculum company. Its suite of solutions includes many well-known, trusted and proven products like SPIREⓇ, Explode The CodeⓇ and Wordly WiseⓇ. The company recently took on a private equity partner committed to product and go-to-market investments that will position the company for explosive growth over the coming years. We are looking for candidates who want to be part of addressing one of our nation’s most critical challenges. Fewer than a third of all students can read proficiently. Reading ability is a key driver of equity since only 20% of Hispanic students, and 16% of Black students can read proficiently by 4th grade. You will be joining a high-paced, creative and fun team dedicated to making a difference in children’s lives and driving equity across our nation’s public schools.\n\nPosition Summary\n\nWe are seeking a dynamic and experienced education sales professional to join our rapidly growing team! As an Account Executive, you will play a pivotal role in driving the success of our research-based K-12 ELA and math products in your territory and the surrounding area. Your primary focus will be on consultative selling, providing valuable solutions to current and potential clients through face-to-face and virtual meetings. Join us and make a significant impact on education by promoting effective print and digital educational resource programs.\n\nResponsibilities\n\nAs an Account Executive, you will have the opportunity to work with K-12 educational programs based on the latest research. By promoting these resources, you will contribute to improving literacy and math skills among students, making a lasting impact on their educational journey. Your role goes beyond sales. You will act as a trusted advisor to clients, providing problem resolution, product updates, and coordinating training for their staff. By delivering exceptional customer service and support, you will build strong, long-lasting relationships with clients. You will have the opportunity to represent our organization at local, state, regional, and national conferences and exhibits. Travel to various educational institutions, conferences, and exhibits within your assigned territory, allowing you to engage directly with educators, administrators, and decision-makers. Join our collaborative sales team and benefit from the collective knowledge and experience of your peers. We value independent thinking and encourage proactive problem-solving. You will have the opportunity to enhance your skills, develop professionally, and contribute to the success of the team. Utilize your solid knowledge of current K-12 structured literacy best practices to effectively demonstrate the value of our educational intervention products. Deliver engaging sales presentations tailored to different stakeholders, showcasing how our products can meet their specific needs and drive positive outcomes. Organize and transport all necessary sales and marketing materials, ensuring you have everything you need to make compelling presentations and facilitate informed decision-making. Follow up promptly on sales leads from various sources and document your activities to maintain a clear sales pipeline. Craft professional correspondence with potential leads and new clients, including responding to catalog and sample requests, as well as generating accurate price quotes. Provide regular sales forecasts based on adoption schedules, presentations, and both new and re-order business, enabling effective planning and resource allocation. Leverage Customer Relationship Management (CRM) software to document client interactions, maintain an accurate pipeline, and analyze opportunities for continuous improvement. \n\nQualifications\n\nHold a bachelor's degree in education, business or a related field, demonstrating your commitment to excellence. Possess 2 to 4 years of prior experience selling K-12 curriculum, with a preference for experience in ELA. Show a track record of consistent sales success over the last 3+ years, highlighting your ability to achieve and exceed targets. Prior teaching or education administration background is a strong advantage, underscoring your deep understanding of the challenges educators face. Exhibit strong analytical and organizational skills, enabling you to proactively solve problems and identify opportunities for improvement. Possess exceptional interpersonal and communication skills, both written and verbal, empowering you to build rapport with clients and deliver impactful presentations. Be an independent thinker while actively contributing to a collaborative sales team environment, fostering innovation and shared success. Display effective public speaking abilities, enabling you to engage and present confidently to large groups, both in-person and virtually. Hold a valid driver's license, maintain an acceptable driving record, and possess an automobile. Travel Requirements: Anticipate travel ranging from 30% to 80% of the time, depending on seasonality, including regular overnight trips and occasional weekends. Emphasize the exciting opportunities for networking and professional growth that come with attending conferences and exhibits. \n\nBenefits and Perks\n\nWe offer a competitive compensation package based on various factors including but not limited to qualifications, skills, competencies, location, and experience. Other rewards include an annual bonus or commission, a 401(k) retirement plan with employer match, medical, dental, and vision insurance effective day 1, generous PTO, sick and paid holidays, as well as employer paid life and short & long term disability insurance. We provided you with a laptop for your home office and a flexible remote-first work culture. \n\nDon’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. Our organization is dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply! You may be just the right candidate for this or other roles. Please be sure to attach your resume. Applications without an attached resume will be considered incomplete. We are an Equal Opportunity Employer.NaNNaNSpokane, WA90552133.03.0NaNNaNFull-timeNaN1.713572e+121.0https://www.linkedin.com/jobs/view/3906267131/?trk=jobs_biz_prem_srchhttps://epsoperations.bamboohr.com/careers/122?src=LinkedInOffsiteApply1.716164e+12NaNMid-Senior levelNaN1.713572e+12epsoperations.bamboohr.com0FULL_TIMENaNNaNNaN99201.053063.0
1238473906267195Trelleborg Applied TechnologiesBusiness Development ManagerThe Business Development Manager is a 'hunter' that carries out tasks that support business growth. Their job is to analyze market trends and identify areas for improvement. This may include but is not limited to obtaining new suppliers to elevate product quality and coordinating new marketing initiatives to expand the company’s customer base. They may also support upper management and increase sales by organizing and participating in meetings between clients and company executives.\n*** A strong background in the oil and gas industry, with established relationships and the ability to network within the oil and gas industry is crucial for this position. ***\nThe person in this role will maximize the sales opportunities for all the Company’s Products for a designated product group area (Dry Foam). Consistent with the Company’s overall business objectives and good business practices, maintains and increases the Company’s sales and profitability through identifying new business opportunities, e.g., new clients and markets, for the Company’s products.\nRepresent Trelleborg Offshore US, Inc. as single point of contact for an assigned product group area – Dry Foam.Develop relationships with clients in defined markets and accounts to generate ongoing profitable businesses like oil & gas.Identify existing RFQ within known markets and identify new business opportunities that align with Trelleborg’s core competences within defined accounts.Liaise and coordinate sales activities as required with other Trelleborg personnel.Track and report on competitors within defined markets such as oil & gas, fire reduction and suppression.Work with clients to define terms & conditions of sale, non-disclosures & legal documents such as contracts or agreements.Visit with new & existing customers, establish relationships with potential new customers & clearly document all such contacts.Travel as required to secure business and update customer business communication(s) with Salesforce® software.Assist with the preparation of and attend trade exhibitions as required.Assist and support the Business Unit with the preparation of press releases and promotional material for the Company’s products.Deliver presentations to clients and management team as needed.Assist as requested other sales personnel and Business Units according to the needs of the business.Follow up with clients on completed projects to document client satisfaction and ensure a system of continuous improvement.Ensures a continuous improvement in both personal and staff's level of knowledge and expertise in relevant industry developments so as to maintain the Company’s reputation and position in its field.Gather and report on market data and trends to prepare sales forecasts to predict future business opportunities.\nQualificationsDecision MakingCommercial AwarenessCustomer Knowledge & FocusBusiness systems, CRM system, videoconferencing, social media as a business toolProject Management SkillsTechnical Product KnowledgeEstablished relationships working with oil & gas, fire reduction and suppression application companies.Proven track record of long-term relationship and technical sellingSome college or technical background would be preferred.Industry background with some manufacturing industry experienceExperience working with distributors and manufacturers’ reps desiredAny polymer or composites product exposure will be preferred but not requiredMust be willing and able to travel up to 50% of the time.NaNNaNTexas, United States2793699.04.0NaNNaNFull-timeNaN1.713573e+121.0https://www.linkedin.com/jobs/view/3906267195/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.716165e+12NaNNaNNaN1.713573e+12NaN0FULL_TIMENaNNaNNaNNaNNaN
1238483906267224SolugenixMarketing Social Media SpecialistMarketing Social Media Specialist - $70k – $75kSan Juan Capistrano, CADirect hireJob ID 2024-9752\nWe are looking for a Marketing Social Media Specialist. This is a direct hire opportunity based out of San Juan Capistrano, CA.\nMarketing Social Media Specialist will report to the Senior Manager of Digital Marketing, needs to be experienced in the publication of a brand’s content through social channels. The Marketing Specialist, will be responsible for creating content as well as identifying appropriate content types, topics, tone, and timing of content across all media channels. In this role, you will need to evaluate the appropriate tone and content for both B2B and B2C channels, while adhering to company brand guidelines. This individual will also be required to manage several social media campaigns around the brand and multiple product lines.\nQualifications:3-5 years of experience in developing and analyzing digital and social media marketing campaigns.Undergraduate degree in communications, marketing, advertising, media studies, business, and/or related fields.Understand social and content best practices to improve the performance of each campaign.Strong analytical skills and ability to provide recommendations based on insights.Excellent written and oral communication skills, consulting skills, and ability to collaborate and work well with others under tight deadlines.Proven experience in video and content creation, with a strong portfolio showcasing open box product content.Proficiency in video editing software and graphic design tools (e.g. Canva, Adobe Illustrator, Adobe Photoshop, Adobe Premiere, etc.)\nResponsibilities: Supports team with the management of social media channels.Work closely with a Digital Marketing Analyst to implement a digital marketing strategy.Take pictures and videos, edit them, and post them on our social media platforms.Maintains and publishes social media calendar in close collaboration with all marketing team members.Communicates social media calendar, and content releases, and reports any changes to our management team.Search and contact different communities and organizations to explore opportunities to reach more customers.Grow our user bases with engaging posts and creative promotions.Defines content requirements, enforces best practices, and suggests ways to improve content for social media channels.Actively engages with consumers across all social platforms (Facebook, Instagram, Pinterest, etc.)Identify opportunities to drive consumer engagement on social platforms.Tracks social performance by developing monthly and quarterly reports, as well as measures and reports on social campaigns.\nAnnual Base Salary Range for CA, CO, IL, NJ, NY, WA, and DC: $70,000 to $75,000. Actual compensation offered may vary depending on factors including but not limited to, position offered, location, education, training and/or experience.\nAbout the Client:Our client is a leading Manufacturer and is based out of South Orange County, California and has locations worldwide.\nAbout Solugenix:Solugenix is an information technology services company known for its deep experience and knowledge in providing comprehensive technology services, solutions, and talent support for companies around the world. The company offers a variety of cutting edge and talent support solutions to promote growth and cutting-edge advancement to our esteemed clients and candidates. We provide these talent support solutions on a contract, contract-to-hire, and direct hire basis. We also have additional resources from our staffing partners to ensure the right match and expertise for the best result.For over 50 years, global and local brands have trusted Solugenix as an added resource and partner in taking steps to ensure their immediate and future success. In addition to generating ground-breaking, industry-defining solutions, Solugenix has been delivering the talent and support needed to make it happen. We are dedicated to partnering with clients and candidates whose core values also foster a culture of professionalism, teamwork, and integrity.75000.0YEARLYSan Juan Capistrano, CA43325.02.0NaN70000.0Full-timeNaN1.713573e+12NaNhttps://www.linkedin.com/jobs/view/3906267224/?trk=jobs_biz_prem_srchNaNComplexOnsiteApply1.716165e+12NaNMid-Senior levelNaN1.713573e+12NaN0FULL_TIMEUSDBASE_SALARY72500.092675.06059.0